Samwel Meja

UNIVERSITY OF NAIROBI DEPARTMENT OF REAL ESTATE AND CONSTRUCTION MANAGEMENT HOUSEHOLDS’ OVER-INDEBTEDNESS AND THE IMPACT IT HAS ON MORTGAGE QUALIFICATION FOR HOME OWNERSHIP. CASE STUDY: FULLY REGISTERED PROFESSIONAL SURVEYORS OF ISK PRACTISING IN NAIROBI. BY MEJA ONDARI SAMWEL B04/4708/2015 A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF THE DEGREE OF BACHELORS OF REAL ESTATE, SCHOOL OF THE BUILT ENVIRONMENT. REAL ESTATE, 2018 I DECLARATION Declaration by the student. I, SAMWEL ONDARI MEJA, declare that this project is my original work and has not been presented for any degree award, in any other University or Institution. Sign ……………………. Date ……………………. Samwel Ondari Meja B04/4708/2015 Supervisor’s Declaration. This project has been submitted for examination with my approval as a University supervisor. Sign ……………….............. Date …………………….... Prof. Syagga Paul M. School of the Built Environment The University of Nairobi. REAL ESTATE, 2018 II ACKNOWLEDGMENT My first gratitude goes to the almighty God for giving me strength and perseverance each day and by standing by me for the entire period I pursued my University’s Degree. My sincere thanks go to my supervisor Prof. Syagga Paul M. for his guidance, support and constructive criticism from the preliminary stages of building a logical flow of research to the final completion of the project. The Professor taught me in my 3rd and 4th year at the university in Research Methods and Professional Practice and Ethics respectively. To all the University’s personnel and staff in the Department of Real Estate and Construction Management, I say thank you. I also thank the Students Welfare Authority (SWA) for ensuring my accommodation in the University’s Halls of residence were safe and hospitable. My heartfelt gratitude to my classmates. It was great knowing you and I will miss the triumphs and tribulations that we shared together throughout the campus life. To my brothers in the University’s Rugby Team, Mean Machine, I say to you “Eschuma Absolute”. Thank you for the trophies we have won together and the losses we have shared. I love you all. Special thanks to my best friend Beverly Atemo Ananda for keeping me motivated when I felt the strenuous toll of the research project. I love you. To my Mum and Dad, I attribute my success to you. I will never thank you enough. To all respondents, I say thank you for your time and patience. God bless you all. REAL ESTATE, 2018 III DEDICATION I dedicate this research project to my beloved Meja family, my parents: Mr. and Mrs. Meja, my brothers and sisters: Janet, Alps, and Celestine. I attribute my success to you all. I am proud to be part of this amazing family. To God be the Glory. REAL ESTATE, 2018 IV ABSTRACT Home ownership is something almost every person on the planet desires. The study identifies that the issue of homeownership is never easy for most households, especially in a country like Kenya where mortgage affordability rates are still low. In addition, the situation is aggravated by households borrowing habits, which may work against them being able to even get a mortgage. Basically, the study sought to identify the various types of credits and loans sourced by households and how these have impacted on their ability to qualify for a mortgage. The research was done using a case study of registered professionals of the Institution of Surveyors of Kenya, whereby the researcher sought to establish whether households are over-indebted by their borrowing habits. This was achieved using a set of indicators that help determine the level of over-indebtedness. The results analysis found that most households satisfied at least two of the indicators of over-indebtedness and hence the study concludes that over-indebtedness does indeed affect mortgage qualification by reducing the income stream that could be used to pay for a mortgage and also by negatively affecting households credit rating and debt-toincome ratio. The key recommendations were based on how mortgage affordability and accessibility can be improved while also giving recommendations on how to best handle households over-indebtedness. REAL ESTATE, 2018 V TABLE OF CONTENTS DECLARATION ............................................................................................................ II ACKNOWLEDGMENT ................................................................................................ III DEDICATION ............................................................................................................... IV ABSTRACT .................................................................................................................... V LIST OF TABLES .......................................................................................................... X LIST OF FIGURES ........................................................................................................ XI GLOSSARY OF ACRONYMS ................................................................................... XII CHAPTER ONE .............................................................................................................. 1 Background information ............................................................................................... 1 The Problem Statement ................................................................................................ 3 Research Hypothesis..................................................................................................... 3 Objectives ..................................................................................................................... 4 Research Question ........................................................................................................ 4 Scope and Area of study ............................................................................................... 4 Assumptions of the study ............................................................................................. 4 Significance of study .................................................................................................... 5 Organization of the study ............................................................................................. 5 CHAPTER TWO.............................................................................................................. 6 LITERATURE REVIEW ............................................................................................. 6 2.1 Introduction ......................................................................................................... 6 2.1.1 Definition of Credit, Loans and Debt .......................................................... 6 2.2 Household Debt .................................................................................................. 7 2.2.1 Sources of household debt........................................................................... 7 Mortgages ......................................................................................................... 8 Car loans ......................................................................................................... 10 Student Loans/School Fee Loan. .................................................................... 10 REAL ESTATE, 2018 VI Advance Salary Loans .................................................................................... 11 Credit Card Loans .......................................................................................... 11 Mobile Money Loans ..................................................................................... 12 Chama Loans .................................................................................................. 12 Microfinance Loans ........................................................................................ 12 Shylocks ......................................................................................................... 13 Co-operative Loans ........................................................................................ 13 Advance Airtime ............................................................................................ 13 2.2.2 Why households take credit/loans and get into debt? ............................... 13 2.3 Household Over-indebtedness. ......................................................................... 15 2.3.1 Definition and Measure of over-indebtedness .......................................... 15 2.3.2 Constraints to determining method of measuring of over-indebtedness ... 18 2.4 Impact of Households’ Over indebtedness on Mortgage uptake ...................... 20 2.5 Mortgage affordability in Nairobi Metropolitan Area ...................................... 21 2.6 Conceptual Framework ..................................................................................... 25 CHAPTER THREE ........................................................................................................ 26 3.1 Introduction .......................................................................................................... 26 3.2 Area of Study ........................................................................................................ 26 3.2.1 Nairobi and Its Metropolis. ............................................................................ 26 3.2.2 The Institution of Surveyors of Kenya (ISK) ................................................ 28 3.3 Research Design ................................................................................................... 28 3.3.1 Target population and sample size ................................................................. 29 3.3.3 Sampling technique ........................................................................................ 30 3.4 Data collection ...................................................................................................... 30 3.4.1 Primary data ................................................................................................... 30 3.4.2 Secondary data ............................................................................................. 31 3.5 Data Analysis and Presentation ............................................................................ 31 REAL ESTATE, 2018 VII 3.6 Ethical considerations ........................................................................................... 32 CHAPTER FOUR .......................................................................................................... 34 DATA ANALYSIS AND INTERPRETATION ........................................................ 34 4.1 Introduction ....................................................................................................... 34 4.2 Analysis of Questionnaires Response ............................................................... 35 4.3 Respondents’ gender. ........................................................................................ 36 4.4 Respondents’ marital status .............................................................................. 37 4.5 Respondents’ level of education. ...................................................................... 38 4.6 Respondents’ employment status. ..................................................................... 39 4.7 Respondents age bracket. .................................................................................. 40 4.8 Households Income Bracket. ............................................................................ 41 4.9 Response to the existence of a credit commitment/loan ................................... 43 4.10 Response to spending more than 30% of monthly income on repayments. ... 46 4.11 Response to credit repayments infringing on ability to fulfill needs. ............. 47 4.12 Response to being 2 months in arrears on credit commitments or household bill. .......................................................................................................................... 48 4.13 Response to considering credit commitments a “heavy burden”.................... 49 4.14 Response to effect of personal credit commitments on Credit Rating and Debt-to-Income-ratio. ............................................................................................. 49 Debt-to-Income-ratio. ............................................................................................. 50 4.15 Response to considering taking a mortgage while considering their credit commitment and income ......................................................................................... 50 3.15 Challenges Encountered ..................................................................................... 51 CHAPTER FIVE ............................................................................................................ 52 CONCLUSIONS AND RECOMMENDATIONS ..................................................... 52 5.1 Introduction ....................................................................................................... 52 5.2 Revisiting the research objectives ..................................................................... 52 5.2.1 Summary of main findings............................. Error! Bookmark not defined. REAL ESTATE, 2018 VIII 5.2.2 Emerging themes. .......................................................................................... 52 5.3 Implication of findings on hypothesis ............................................................... 52 5.4 Conclusions ....................................................................................................... 53 5.5 Recommendations ............................................................................................. 54 5.6 Areas of further study. ...................................................................................... 55 Bibliography ................................................................................................................... 56 APPENDIX: QUESTIONNAIRE TO THE HOUSEHOLDS ....................................... 59 REAL ESTATE, 2018 IX LIST OF TABLES Table 2.1 – Common indicators of over-indebtedness Table 2.2: Mortgage affordability index Table 2.3: Summary of mortgage affordability Table 3.1: Summary Table of Research Methodology Table 4.1 Rate of response to the questionnaires administered Table 4.2: Gender Distribution Table 4.3 Respondents’ marital status Table 4.4 Respondents level of education Table 4.5: Respondents’ employment status Table 4.6 Respondents age bracket Table 4.7 Households Income Bracket Table 4.8 Response to the existence of a credit commitment/loan Table 4.8.1 Response to the type of credit/loan taken Table 4.8.2 Response to total number of loans/credit taken. Table 4.9: Response to spending more than 30% of monthly income on repayments Table 4.10 Response to credit repayments infringing on the ability to fulfill needs. Table 4.11 Response to being in arrears on credit commitments or household bill. Table 4.12: Response to considering credit commitments a “heavy burden” Table 4.13: Response to the effect of personal credit commitments on Credit Rating and Debt-to-Income-ratio Table 4.14 Response to choosing to take a mortgage while considering their credit commitment and income. REAL ESTATE, 2018 X LIST OF FIGURES Figure 2.1: Mortgage Financing Institution Flow Chart Summary Figure 3.1: A Satellite map of Nairobi. Graph 4.1 Rate of response to questionnaires administered Graph 4.2: Respondents gender distribution Graph 4.3 Respondents’ marital status Graph 4.4 Respondents level of education Graph 4.5: Respondents’ employment status. Graph 4.6 Respondents age bracket Graph 4.7 Households Income Bracket. Graph 4.8 Response to the existence of a credit commitment/loan Graph 4.8.1 Response to type of credit/loan taken. Graph 4.8.2 Response to total number of loans/credit taken. Graph 4.9. Response to spending more than 30% of monthly income on repayments Graph 4.10 Response to credit repayments infringing on the ability to fulfill needs. Graph 4.11 Response to being 2 months in arrears on credit commitments or household bill. REAL ESTATE, 2018 XI GLOSSARY OF ACRONYMS ISK: Institution of Surveyors of Kenya CBK: Central Bank of Kenya SPSS: Statistical Package for Social Sciences KBA: Kenyan Bankers Association KPDA: Kenya Property Developers Association HELB: Higher Education Loans Board BOSA: Back Office Service Activities FOSA: Front Office Service Activities ITPA: Transfer of Property Act, 1882, of India. REAL ESTATE, 2018 XII CHAPTER ONE INTRODUCTION Background information The traditional African setup has usually considered land as the optimum measurement of wealth. Land is an important aspect of the life of any society. It is essential for food production and security, supports important biological resources and processes, sustains the livelihoods of the majority of Kenyans, and constitutes an important cultural heritage for many communities (Akech, 2006). In our post-modern society, we have had the benefit to learn about Real Estate. Real Estate is any landed property: land and anything that is firmly attached to it e.g. buildings, minerals, fences etc. In this case, this study focuses on land and the buildings erected on land as a place to stay and live in; a shelter. A shelter is a physiological human need, just as food. So much so that even those who cannot afford it still need it. By its nature housing represents a major investment, requiring a substantial capital outlay (Nabutola, Affordable Housing – Some Experiences From Kenya, 2004). Today, the classical Kenyan dream includes graduating from college with a Higher Education Certificate, getting a job, starting your own business, accumulating wealth and eventually settling down in your own home. According to Modigliani’s life-cycle theory, the very young have little wealth, middle-aged people have more, and peak wealth is reached just before people retire. As they live through their golden years, retirees sell off their assets to provide for food, housing, and recreation in retirement. The assets shed by the old are taken up by the young who are still in the accumulation part of the cycle (Deaton, March 2005). Home ownership is both a social as well as a commercial investment; a home provides shelter as a basic need for security and stands to buttress commercial progress once its value appreciates. Kenyan’s demand for urban housing has been rising in line with the growth in the country’s population, especially among individuals whose income puts them in the middle-class category (Kenyan Bankers Association, 2015). These growing demand has seen the increased investment in the residential real estate sector, for both rental units and owner-occupied homes. Furthermore, prices of land, stand-alone homes, REAL ESTATE, 2018 1 apartments, and rental units have skyrocketed. So much so that, owning a home for an average consumer becomes an inconceivable dream. The journey to home ownership is never an easy affair and may even take a long period of time (years) before the resident can officially secure tenure of his/her home after all financial obligations have been met. One way through which someone can get to be a home owner is by the use of a mortgage as a housing finance option. A mortgage is an important pillar in housing finance that has grown in popularity since the year 2006 when the number of recorded mortgage accounts was 7,275. The number of open accounts grew to 24,458 by year 2015 (Central Bank of Kenya, 2015) and later reduced to 24,085 by year 2017 due to the interest cap rate that saw lenders refusing to disburse mortgages even as the demand for mortgages grew as more Kenyan’s sought to take advantage of the lower interest rates. This phenomenon was because of the low profitability that financiers would accrue vis-à-vis the high risks involved in mortgage lending. Despite this setback, the government of Kenya recognizes that the provision of suitable housing is very central to national development. In respect, one of the main objectives of Kenya’s Vision 2030 is to solve the housing shortage, estimated at 150,000 units annually (Kenya, 2007). This means that Kenya requires approximately 200,000 new housing units annually to meet the demand, yet only 50,000 units are built, leaving the housing deficit growing by 150,000 units per years (Kenya Property Developers Association, June 26, 2018). As a result of the existing incongruous supply and demand forces, housing prices have increased 100 percent since 2004 (Kenya Property Developers Association, June 26, 2018). This, therefore, means that homeownership becomes costlier each year. In addition, consumer lifestyle patterns do not favor easy access to housing finance options especially mortgages, as households’ trends show them spending more than they earn. This means that more and more households are continuing to shackle themselves in severe debt as they opt to finance available credit options so as to streamline their consumption needs. Data from the 2016 Economic Survey, published in May, shows the consumption rate of 102 percent of income in 2013, 103 percent in 2014 and 102 percent in 2015. These percentages include money spent on goods and services, while not including investments. These numbers paint a grim picture as it shows households’ need to borrow money, just to meet their consumption needs alone. REAL ESTATE, 2018 2 The Problem Statement In a society riddled with increasing financial obligations, cost of living and increasing cost of education, being financially aware and responsible is an important life skill. In such respect, being aware of your debt burden and how it will affect you in the future is very important since debt affects millions of people around the world. People usually tend to borrow heavily in young adulthood when they have lower incomes vis-à-vis their large consumption needs (Aspen Institute, 2018). Households’ heavy borrowing as a result of lifestyle pressures may lead overindebtedness thus getting exposed to external financial shocks and psychological strain. Despite households’ borrowing decisions being important for their own well-being, aggregate consumption, asset demand, and financial stability, over-indebtedness becomes a major issue especially when households’ are no longer able to repay their debts as a result of the huge debt burden. In addition, households’ over-indebtedness may prove a hindrance to accessing a mortgage for financing a house/home. Such hindrances come in the form of households’ credit unworthiness and reduction in spendable income due to servicing more than one debt. In view of the above, this study seeks to investigate the role over-indebted households have played in the slow uptake of mortgages in Kenya, being a country of 45 Million people, but only having just slightly over 22,000 mortgage accounts (Central Bank of Kenya, 2015). Research Hypothesis The study is guided by the following hypothesis; I. Null hypothesis (H0): Households’ over-indebtedness does not affect mortgage qualification. II. Alternative hypothesis (H1): Households’ over-indebtedness affects mortgage qualification. REAL ESTATE, 2018 3 Objectives • The general objective of the study is to investigate the impact of households’ over-indebtedness on mortgage qualification for home ownership. Specific Objectives 1. To identify various sources and types of household credit and loans. 2. To determine a measure for household over-indebtedness. 3. To identify the impact of households’ over-indebtedness on mortgage uptake 4. To establish mortgage affordability for various households. Research Question 1. What are the various types of household debt that a modern consumer faces? 2. How will I determine the measure of household over-indebtedness? 3. What is the impact of households’ over-indebtedness on mortgage uptake? 4. How accessible/affordable is loan mortgage among the various households? Scope and Area of study This study focuses on home buyers in the real estate market in the Nairobi Metropolitan area, Kenya, because of the high rate of population growth that backs the increasing demand for urban housing modes. This makes it a suitable testing ground for examining the relationship between household’s over-indebtedness and how that affects qualification for a mortgage for home ownership. Assumptions of the study The study is based on two assumptions i.e. ✓ The area of study chosen is typical and findings can be applied to any other place regardless of such differences as geographical location. ✓ Data collected from secondary sources is accurate and reliable. REAL ESTATE, 2018 4 Significance of the study The study is intended to show the importance of owner-occupied homes and how the enjoyment of this benefit may be limited by the situation of being overwhelmed by debt burdens, both secured and unsecured. The study will be useful in creating an awareness and understanding of household debts (credits) and mortgage as a housing finance option. Organization of the study The project comprises of five sections as follows: Chapter one is introductory. It contains the background of the study, problem statement, hypothesis, objectives, research questions, significance and scope of the study. Chapter two discusses the literature reviewed. This acts as the foundation for the research and provides a benchmark for the evaluation of research data. It includes some information on debt, types of household debts; including mortgages, various definitions of over-indebtedness and how to measure it. Lastly, it touches on mortgage affordability for households living in the general Nairobi metropolitan area. Chapter three gives a brief description of the study area and highlights the sample, sampling procedures and methods (tool of research) used in the collection and analysis of data. Chapter four covers the analysis of data, hypothesis testing, and presentation of results based on the study objectives. Chapter five constitutes conclusions based on the study, recommendations, and areas of further studies. REAL ESTATE, 2018 5 CHAPTER TWO LITERATURE REVIEW 2.1 Introduction This chapter deals with information and data obtained from various sources. It delves into relevant studies that have been done before. The chapter starts by looking into different types and sources of credits and loans including mortgages; which is a housing finance loan. It then explores how the level of a households’ over-indebtedness can be measured and the shortcomings of various indicators to be used in determining overindebtedness. The chapter concludes by looking into mortgage accessibility/affordability as a pillar that supports the journey to homeownership and how it has been affected by households being over-indebted. 2.1.1 Definition of Credit, Loans, and Debt Credit is the provision of money, goods, or services with the expectation of future payment (Merriam-Webster, 2018). Therefore, credit is the promise to deliver money at a later date. Credit spends just like money i.e. when you pay with money the transaction is settled; but if you pay with credit, the payment has yet to be made. Money is what you settle your payments with, in any transaction (Dalio, 2014). Loans are money lent at an interest via a contractual agreement between the Lender and the Borrower. Loans are most useful when a significant purchase is going to be made or when the amount of money needed is known in advance (Banco, 2018). A major difference between a loan and a credit is that credit operates more as a source of income for occasional support for expenses that come up unexpectedly (Banco, 2018). These two words are occasionally used interchangeably but it is important to note in this case that, loans are usually larger in sum, taken over a longer term, have a higher interest rate and are ordinarily secured with some form of collateral. Debt is defined as a state of being under obligation to pay or repay someone or something in return for something received (Merriam-Webster, 2018). One can get into debt either by taking loans or financing purchases through credit. REAL ESTATE, 2018 6 2.2 Household Debt Household debt is money borrowed by individuals in the form of loans that are to be repaid later. Individuals are collectively referred to as the household sector in economic data. The total sum of all the various types of outstanding loans in the economy, such as; mortgages, unpaid balances on credit cards and so on, is what economists refer to when they use the term household debt (Harari, 2018). 2.2.1 Sources of household debt Household debt can be broadly grouped into two categories: secured and unsecured debt. Secured debt is a loan secured on an asset that serves as the collateral. The most obvious example of secured lending is mortgages. The house being purchased acts as the collateral. Unsecured debt is lending provided to individuals and is not secured on an asset. Credit card lending is the most prominent example. Personal loans, student loans and loans from payday lenders (advance salary) also come under this category. (Harari, 2018) The specific types of household debts taken in Kenya include: • Mortgages – Housing Finance Loans • Car loans – Financing Purchase of a Car • Student loans/School Fee Loan – i.e. HELB & Banks. • Advanced Salary Loan – Pay once you get your salary • Credit Cards – Plastic Money it ensures there is money in your wallet all the time • Co-operative – Various products (BOSA and FOSA) • Microfinance Loans – Groups Model • Chama Loans – Member Based Loans • Shylocks – Need-Based Lending • Mobile Money Loans E.g. M-Shwari, Tala etc. • Advance Airtime – Okoa Jahazi (Safaricom), Kopa Credo (Airtel) & Pewa (Telkom). (Miriongi, 2016). REAL ESTATE, 2018 7 Mortgages A mortgage is a legal agreement to borrow money from a bank or other financial organization, especially to buy a house or other property (In Cambridge Dictionary, 2018). Therefore, a mortgage is a loan whose collateral is real estate property and requires payments to be made in installments till the debt has been repaid in entirety. The two main categories of mortgages are fixed rate (fixed payment) mortgage and the adjustable rate (variable payment) mortgage. A fixed rate mortgage is a mortgage that locks in the borrowers' interest rate and thus the required monthly payments over the life of the mortgage regardless of how the market rate changes whereas an adjustable rate mortgage is a mortgage in which the interest rate is tied to some market interest rate. Thus, the required monthly payment can change over the life of the mortgage. These mortgages include; adjustable rate mortgage, graduated payment mortgage and growing equity mortgage. A mortgage on a home is one of the largest debt any household in Kenya could incur. In Kenya, the statutory definition of mortgages is found in the TRANSFER OF PROPERTY ACT, 1882, OF INDIA. Section 58 of ITPA defines mortgage as the transfer of an interest in specific immovable property for the purpose of securing the payment of money advanced or to be advanced by way of loan, an existing or future debt, or the performance of an engagement which may give rise to a pecuniary liability (Kenya Legal Resources, 2018). Under section 58 of the ITPA there are four classes of legal mortgages and Section 58 (5) lists those classes as follows: • Simple mortgage - Where, without delivering possession of the mortgaged property, the mortgagor binds himself personally to pay the mortgage-money, and agrees, expressly or impliedly, that, in the event of his failure to pay according to his contract, the mortgagee shall have a right to cause the mortgaged property to be sold and the proceeds of sale to be applied, so far as may be necessary, in payment of the mortgage-money, the transaction is called a simple mortgage and the mortgagee a simple mortgagee (The Republic Of Kenya, 2010). • Mortgage by conditional sale - Where the mortgagor ostensibly sells the mortgaged property on condition that on default of payment of the mortgage money on a certain date the sale shall become absolute, or on condition that on REAL ESTATE, 2018 8 such payment being made the sale shall become void or on condition that on such payment being made the buyer shall transfer the property to the seller (The Republic Of Kenya, 2010) • Usufructuary mortgage - Where the mortgagor delivers possession of the mortgaged property to the mortgagee, and authorizes him to retain such possession until payment of the mortgage-money, and to receive the rents and profits accruing from the property and to appropriate them in lieu of interest, or in payment of the mortgage-money, or partly in lieu of interest and partly in payment of the mortgage-money, the transaction is called a usufructuary mortgage and the mortgagee a usufructuary mortgagee (The Republic Of Kenya, 2010). • English mortgage - Where the mortgagor binds himself to repay the mortgagemoney on a certain date, and transfers the mortgaged property absolutely to the mortgagee, but subject to a proviso that he will retransfer it to the mortgagor upon payment of the mortgage-money as agreed, the transaction is called an English mortgage (The Republic Of Kenya, 2010). In Kenya, all financial institutions have opted to recognize the most common classification of mortgages: Fixed rate & Adjustable rate mortgage. Mortgage loans are made available by both Commercial banks and Mortgage Financial institutions that may either fall under Public or Private Financial institutions as summarized below in the flowchart; REAL ESTATE, 2018 9 Figure 2.1: Mortgage Financing Institutions Flow Chart Summary Commercial Banks & Mortgage Financial Institutions Public Financial Institutions Private Financial Institutions Locally Owned Consolidated Bank of Kenya Ltd Development Bank of Kenya Ltd National Bank of Kenya Ltd Foreign (50% Control) Commercial Banks Commercial Banks (27) Mortgage Financial Institutions (1) (15) Source: Central Bank of Kenya, 2018. Car loans A car loan, also known as an automobile loan or auto loan, is a sum of money a consumer borrows in order to purchase a car (Gale, 2008). Apart from a home, a car is one of the biggest single purchases one is ever likely to make. Auto loans can be accessed through almost all commercial banks in Kenya. The diverse financial institutions in Kenya offer a variety of auto loan financing, ranging from partial financing to 100% financing on motor vehicles. Car loans follow similar rules and procedures that apply to other loans i.e. when taking out a loan a borrower agrees to pay back the full loan amount, as well as any interest. Student Loans/School Fee Loan. Student loan is a loan that a student at college or university agrees to take/borrow from a bank or financial institution to pay for their education and then has to pay the money back after they finish studying and start working (Cambridge Dictionary, 2018), whereas a school fee loan is monies taken by a parent or guardian from a financial so as to be able to meet the cost of paying for his/her child’s education. REAL ESTATE, 2018 10 This kind of financial assistance comes in very handy in situations where households can no longer effectively meet the cost of tuition. Student loans have even grown to be an accepted norm in the larger student population in Kenya, as more individuals apply for them as the opportunity arises. It has become a necessity for many students to take students loans so as to complete their higher education program and invest in themselves the necessary skills and knowledge they will require in future or in the job market. This has proven to be a wise decision in many cases as the investment has paid off many times over, especially after the loan has been settled. The issuing of student loans in Kenya is handled by the Higher Education Loan Board (HELB), which was established in July 1995 by an Act of Parliament ‘Higher Education Loans Board Act’ Cap 213A. Whereas school fee loans can be accessed from various financial institution including commercial banks and microfinance institutions. Advance Salary Loans A salary advance is a payment issued to an employee in the form of a short-term loan, on a date in advance of the employee’s regularly scheduled payday, for emergency situations especially after an employee has exhausted all other options or available resources. The number of times an employee can take an advance limited, for example, once or twice each year. The amount of the advance is usually calculated as a percentage of the net forthcoming wages. There are usually policies set in place that define the kind of employees eligible for salary advances. The policies may require employees to work for a firm for a specific period of time, have no disciplinary issues and meet other set qualifications. Credit Card Loans A credit card loan is money one borrow when they use their credit card. Credit cards allow individuals to buy things when they either don’t have cash or don’t want to use cash. Individuals may also prefer to pay by credit card because it offers convenience, security, and easy tracking. When one buys something using their credit card the issuer (financial institution) loans the money to them, to make the purchase. After which they are obligated to pay back the loan at a later date, especially after a grace period (Market Business News, 2018). REAL ESTATE, 2018 11 With most credit card loans, the user (borrower) does not need to use any property as security, hence, therefore, making it an unsecured form of a loan. If the borrower defaults, the credit card issuer has no recourse to a rapid way of recouping its loss. Credit card issuers usually use a system of customer’s credit rating to determine whether to lend them money and how much to lend. Mobile Money Loans Mobile money lending is facilitated and made possible by an eleven-year-old money transfer service mainly targeting unbanked people. The service was launched in 2007 by Safaricom, the country’s largest mobile telephone operator. After a few years, the service through which people could send money via a simple Short Message Service, captured the attention of the world as other networks and countries rushed to adopt it (Standard Digital, 2013). The money transfer service has since then been adopted by financial institutions to incorporate both banked and unbanked people. The mobile money transfer service led to the innovation of mobile money loans, which in turn led to the complete revolution of the financial sector as their capacity to enlarge their market share soared. Many people can now get easy access to credit via different mobile money lending platforms and loan apps; including Mshwari, KCB Mpesa Loan, Branch, Tala, Zidisha, Eazzy Loan, Saidia, Kiva, Kopa Leo among others. Chama Loans A Chama is an informal cooperative society (self-help group) formed by willing participants with the intention to pool their resources and invest their savings, with the mutual goal to improve the living standards of its members. A chief characteristic of Chamas is the Merry-go-round financial benefit, where individual members of the group get to enjoy pooled financial resources in turns. Chama loans arise when the society opts to take a loan from a financial institution based on their total savings, with an advanced loan up to 6 times the group savings (Bank of Africa, 2018). Microfinance Loans Microfinance loans, also called microcredit, is a type of banking service that is provided by microfinance institutions, to unemployed or low-income individuals or groups who otherwise would have no other access to financial services (Investopedia, 2018). REAL ESTATE, 2018 12 Microfinance institutions are governed by an act of parliament, ‘Microfinance Act, 2006’. Which gives guidelines, the basic definition of terms, licensing provisions and termination and other provisions relating to their governance and supervision. Shylocks Shylock is a term used to describe someone who lends money at excessive rates of interest. In Kenya, the term shylock is used to refer to loan sharks. A loan shark is a person or body that offers loans at extremely high interest rates. Loan sharks sometimes enforce repayment by blackmail or threats of violence. Shylocks often prey upon those who find themselves in a desperate situation and feel they have no alternative but to resort to the loan shark industry to obtain credit. Co-operative Loans Co-operative loans, provided by Savings and Credit Societies (SACCOs), come in a double package. The BOSA (Back Office Service Activities) and FOSA (Front Office Service Activities). Advance Airtime Advance airtime is a service provided by mobile network providers whereby you can request to recharge your account in advance without paying, at least for that moment, the advance is usually meant to be repaid later. This is ideal for when you’re unable to recharge your mobile account especially when out of cash or shops are closed. Examples include Okoa Jahazi (Safaricom), Kopa Credo (Airtel) & Pewa (Telkom). 2.2.2 Why households take credit/loans and get into debt? According to the life-cycle theory, households apply to credit markets because they want to have steady living conditions over the years (D’Alessio & Iezzi, 2012). Individuals borrow money to facilitate and enhance their purchasing power or satisfy a pressing need at the time. Other households take credit to pay for emergency health services and situations as they arise. The availability of credit allows people who do not have the upfront money needed to be put in use now, to borrow it today knowing that they will have sufficient income to pay off that loan in installments in the future. The various factors that move people to take loans include; REAL ESTATE, 2018 13 • Reduced income: Often when expenses and financial obligations exceed income, households could opt to take loans to meet such obligations. • Irregular income streams: These scenario reduces one’s confidence and dependency on his income stream and would lead him to source for small loans and credits at one time or the other in order to support himself/herself. • Poor money management: Most of the time, poor budgeting invokes debt. • Medical expenses: Expensive medical treatments make this one of the easiest ways to fall into debt. • Divorce: Single parents take debts easily as their financial power is reduced when couples separate, this is more significant when one couple depended on the other. • Little savings or no savings to meet emerging needs and necessities is also a reason why people could get into debt • Gambling: As loans are easily available today, one becomes easily addicted to the idea of “winning big” and striking it rich. In retrospect, there are other features or factors of loans and credit that influences households’ decisions and preferences for taking on debts. These include; o Size of the loan – Smaller loan bundles are more preferable and cheaper to source in comparison to Large loan amounts. Furthermore, they are affordable to all income groups. o Interest rate – Lower interest rates charged on particular loan types makes them preferable due to their cheap nature. High rates make loans and credits expensive and to some unaffordable o Security/collateral – Loan types and credits not requiring collateral are more frequently sourced especially in the non-property owning household individuals such as students and low-income group individuals. o Eligibility criteria – Strict eligibility criteria for specific types of loans such as mortgages and car loans make them only accessible to various income groups while locking out low-income groups. o Repayment period – Large loans have a significantly long repayment period requiring debtors to be financially obligated for a lengthy period of time, less to their liking. Short term loans are more conveniently tailored to be sourced and REAL ESTATE, 2018 14 paid back within a shorter time frame, hence they offer a surety that long-term loans can never provide. 2.3 Household Over-indebtedness. 2.3.1 Definition and Measure of over-indebtedness One of the objectives of this research is concerned mainly on finding a methodological approach of measuring over-indebtedness of Kenyan households. The achievement of this objective will ensure that the researcher adds to the existing body of knowledge. To ensure that this objective is met, it is important to examine the types of households; in terms of their income level, and other characteristics that are likely to result in households being over-indebted; in the sense of having difficulty in servicing their existing debt burden. Over-indebtedness has many literary definitions and there exists not a widely and commonly accepted definition to best describe when a household is over-indebted (Bridges & Disney, 2004) or, consequently on how to measure it. The European Commission recognized this knowledge gap and hence dedicated its efforts towards achieving a common operational European definition of over-indebtedness (D’Alessio & Iezzi, 2012). In their study (European Commission, Towards a common operational European definition of overindebtedness., 2008a) they examined and compared definitions and measures of over-indebtedness in the European Union countries, and underlined the different points of view emerging from the different socio-economic and legislative backgrounds. For example, in France, an individual is considered over-indebted when, with wellmeaning intentions, he/she is unable to meet the obligations coming from debts obtained for non-professional reasons. Whereas, Germany defines over-indebtedness as a situation where household income “in spite of a reduction of the living standard, is insufficient to discharge all payment obligations over a long period of time” (Haas, 2006). In the existing wide variety of official national definitions of over-indebtedness, the European Commission study identified some features common to all countries. REAL ESTATE, 2018 15 These features include: • The economic dimension (amount of debt to repay), • The temporal dimension (the time frame for servicing the debt) • The social dimension (the basic expenses that have to be met ahead of the repayment of the debts) and • The psychological dimension (the stress that over-indebtedness causes). Further studies carried out for the European Commission to develop a common definition across the European Union countries, identified a set of criteria to be applied in determining household over-indebtedness (European Commission, Research note 4/2010, 2010). The study highlighted that: • The unit of measurement should be the household because the incomes of individuals are usually pooled within the same household; • Indicators need to cover all aspects of households’ financial commitments: borrowing for housing purposes, consumer credit, to pay utility bills, to meet rent and mortgage payments and so on; • Over-indebtedness implies an inability to meet recurrent expenses and therefore should be looked at a structural basis rather than a temporary state; • It is not possible to resolve the problem simply by borrowing more; • For a household to meet its commitments, it must reduce its expenses substantially or find ways of increasing its income. Based on the above set of criteria, a household is considered over-indebted when its existing and expected income/resources are insufficient to meet its financial commitments without lowering its standard of living. This definition of overindebtedness is widely accepted in principle but in practice, it is very difficult to identify households in such a situation (D’Alessio & Iezzi, 2012). In addition to the above information, existing studies identified a common set of indicators which function to capture the true state of a household’s over-indebtedness. These indicators broadly reflect four aspects of over-indebtedness. They include; making high repayments relative to income, being in arrears, making heavy use of credit and finding debt a burden (see Table 1). REAL ESTATE, 2018 16 Table 2.1 – Common indicators of over-indebtedness Category Indicator Households spending more than 30% (or 50%) of their gross monthly income on total borrowing repayments (secured and unsecured). Cost of servicing debt Households spending more than 25% of their gross income on unsecured repayments. Households whose spending on total borrowing repayments takes them below the poverty line. Household more than 2 months in arrears on a credit commitment Arrears or household bill. Number of loans Households with 4 or more credit commitments. Subjective perception of burden Households declaring that their loan repayments are a “heavy burden” Source: (D’Alessio & Iezzi, 2012) In the above document (D’Alessio & Iezzi, 2012), the researchers identified the underlying assumptions and facts in the above the indicators. These include: • First two indicators capture the burden imposed by debt repayments and put arbitrary limits on repayments relative to gross income, beyond which they are thought to represent a significant burden for households. • 50 percent is identified as the limit for the ratio of the cost of debt to income beyond which repayments are a major burden for households (DeVaney and Lytton, 1995). • When considering only unsecured loans, the limit drops to 25 percent. This indicator is based on the fact that the risks connected with collateralized debts are basically covered by real assets, thus the analysis must be restricted to unsecured loans. REAL ESTATE, 2018 17 • The arrears indicator captures all forms of debt and household bills for which a household is more than two months overdue. The cut-off is chosen in such a way that households simply forgetting to pay a bill or debt for one or two months are not considered to be over-indebted (Oxera, 2004). 2.3.2 Constraints to determining the method of measuring for over-indebtedness The major constraint of establishing a measure of households’ over-indebtedness is that it will always be subjective, and thus prone to error due to different people’s interpretations of whether or not they are facing loan repayment difficulties. In addition to this, all of the indicators presented above suffer from a variety of problems and such a set of indicators will also need to operate within the limits of the available data (D’Alessio & Iezzi, 2012). For example, considering the repayment-to-income ratios when servicing debts. Repayment-to-income ratio offers an apparently simple way of measuring overindebtedness, but it has its own shortcomings. There are questions as to whether an increase in borrowing, which implies an increase in the repayment-to-income ratio, is driven by households who can afford it. This is to say, debt can increase relative to income without this necessarily making debt management problems more profound if the increase occurs predominantly among households with high levels of income. Meaning that they can potentially bear a debt burden higher than 30 percent of their income. In addition, debt-to-income ratio measures typically ignore household assets (D’Alessio & Iezzi, 2012). Households might accept a debt burden of more than 30 percent if they can rely on financial assets worth more than their outstanding debts: it appears unrealistic to classify such households as over-indebted. Furthermore, while an increase in outstanding debt might be accompanied by growing difficulty in servicing the loans, it might be accompanied by an increase in the value of assets which are often the counterpart of the debt. In other words, households might be able to meet their debt servicing obligation by selling some of the assets (D’Alessio & Iezzi, 2012). Furthermore, the availability of assets may allow households with heavy debt burdens to access new credit. An expansion of credit should make it easier for households to manage their debt and cope with a temporary reduction in income. REAL ESTATE, 2018 18 The use of data on arrears in making payments proves to be a constraint in many cases, especially when it is necessary to judge the seriousness of the arrears and determine the point where over-indebtedness begins. This, in turn, depends on the social and economic conditions in the country and the financial circumstances of the household. Furthermore, by looking only at the households currently unable to repay their debts, this measure may overlook those who still manage to meet their financial obligations, but who have borrowed so much that they have become vulnerable to external financial shocks e.g. increase in interest rates or a temporary loss in income. Arguably, such households can also be considered over-indebted (D’Alessio & Iezzi, 2012). The criterion based on the number of credit commitments might not reliably detect situations of over-indebtedness since a large number of outstanding small debts does not necessarily imply a condition of difficulty. Similarly, being behind in the payment of small amounts might not correspond to a condition of over-indebtedness. The over-indebtedness indicator that identifies the households that, after taking account of the spending on total borrowing repayments, are below the poverty line has the great advantage of referring to a commonly accepted threshold: the poverty line (D’Alessio & Iezzi, 2012). The fact that each of these indicators addresses different aspects of overindebtedness, they each provide potentially valuable information to be used in this research. However, none of them is ideal in the sense that it prevails over all the others. In conclusion, considering the difficulties associated with most indicators of overindebtedness, the most elaborate method is to ask people directly whether or not they are facing debt repayment difficulties. The drawback with these subjective indicators is that they inevitably depend on individuals’ interpretation of terms such as “heavy burden” and “repayment difficulties”, which is likely to vary between individuals. REAL ESTATE, 2018 19 2.4 Impact of Households’ Over-indebtedness on Mortgage uptake Mortgage financing requires high eligibility criteria that literary locks out the low and middle-income earners from accessing this opportunity (Nabutola, Affordable Housing – Some Experiences From Kenya, 2004). This, therefore, means that mortgage financing is still yet to reach the low and middle-income markets, especially at such a time when suitable, decent and affordable housing modes need to be made available. In this case, the researcher opted to direct his study to the individual households with the capability to access a mortgage but have not been able to do so, due to one reason or the other, but chief among them being the reason of being over-indebted. One of the impacts of being in debt, without necessarily being over-indebted, is that your total spendable income is reduced each time a debt is serviced. This means, therefore, households’ income left after servicing all other debts and credit commitments may not be enough to go into servicing a mortgage for a home. In this study, any other debt serviced apart from a mortgage loan for house purchase is taken as a factor contributing to the overall failure of qualifying for a mortgage. As a general rule of thumb, monthly housing payment for any rational consumer should not exceed 28 percent of gross income (Rothenbuescher, 2017). This conclusion is derived from the 28/36 Rule; the 28/36 Rule is the rule-of-thumb for calculating the amount of debt that can be taken on by an individual or household. The 28/36 Rule states that a household should spend a maximum of 28 percent of its gross monthly income on total housing expenses and no more than 36 percent on total debt service, including housing and other debt such as car loans (Investopedia, 2018). Therefore, in light of the above, it is apparent that households’ debts (non-mortgage) will definitely have an impact on mortgage uptake in the sense that it will affect households’; ✓ Credit Score & ✓ Debt to Income Ratio. These two are among the primary factors that lenders will look at before approving credit or mortgage applications. Lenders will require that the credit score and debt-to-income ratio fall within a certain range. REAL ESTATE, 2018 20 A credit score is a statistical number that evaluates a consumer's creditworthiness and is based on credit history (Investopedia, 2018). Lenders use credit scores to evaluate the probability that an individual will repay his or her debts. The higher the score is the more financially trustworthy the individual is considered to be. Over-indebted households are always at risk of getting behind on their debt repayments which eventually will result in a tarnished credit score/rating. The debt-to-income ratio is a personal finance measure that compares an individual’s debt payment to his or her overall income (Investopedia, 2018). Lenders, use this measure to estimate an individual’s ability to manage monthly payment and repay debts. Debt-to-income ratio is calculated by dividing total recurring monthly debt by gross monthly income, and it is expressed as a percentage. Having a low debt-to-income ratio demonstrates a good balance between debt and income and hence such individuals are preferable to mortgage lenders. 2.5 Mortgage affordability in Nairobi Metropolitan Area Mortgage affordability is established and measured using a tool known as the Mortgage Affordability Index. It measures whether the average income earned by a household is enough to enable a household to purchase a house with a mortgage option. (Cytonn Research, 2017). In the computation of these indices, the key factors under consideration are household income, house prices, locations in Nairobi and the Metropolis, and monthly payments for mortgages and rent. The index value is obtained by dividing the qualifying income by the median household income (Cytonn Research, 2017). The qualifying income is obtained by dividing the monthly mortgage payments by 40%. The assumption being made here is that households spend a maximum of 40% of their income on mortgage payments; and an index of 100 or above indicates the most affordable areas, while that of below 100 to 0 indicates the least affordable areas. REAL ESTATE, 2018 21 Table 2.2: Mortgage affordability index Nairobi Metropolitan Mortgage Affordability Index House Median Monthly Mortgage Segment High Income Upper Middle Lower Middle Satellite Towns Price per Monthly Square Household Payment Meter Income Qualifying Mortgage Household Income Affordability Index 197,706 1,300,000 1,156,241 2,890,603 45 129,165 450,000 301,139 752,848 62 85,745 200,000 138,720 346,800 68 66,628 200,000 106,751 266,879 82 325,000 219,930 549,824 65 Average 107,455 With an average index of 65, mortgages are unaffordable across all segments due to the high prices of houses and cost of debt (Cytonn Research, 2017). Terms Median monthly household income - this is the median of the monthly income earned by households in the area under consideration. Monthly mortgage payment - this is the monthly contribution that a household makes to the mortgage lender to service the loan. It is calculated using the Excel PMT function based on the house price, at a 15% interest rate and a 20 -year term Qualifying household income - this is the monthly income that a household needs to earn to be able to afford a mortgage on a house REAL ESTATE, 2018 22 Based on Cytonn’s Research, the table below gives a summary of mortgage affordability in the Nairobi Metropolis, depending on households’ income levels: Table 2.3: Summary of mortgage affordability Summary and Conclusions –Mortgage Affordability Household Income Bracket (Kshs) Towns in which mortgages are affordable to households for the stated income levels Thindigua, Kiambu, Athi River, Komarock, 150,000-300,000 Dagoretti, Thika Kitengela, Ruaka, Rongai, Donholm Kikuyu, Juja, Ruiru , Ngong , Mountain View, 300,000- 1,000,000 Imara Daima, Kasarani, Langata, Upper Hill, Kilimani, Lavington, Kileleshwa, Westlands, Runda Mumwe, Redhill Above 1,000,000 Lower Kabete, Loresho, Ridgeways, Riverside, Roselyn , Karen, Runda, Kitisuru It was noted that satellite towns such as Thindigua, Kiambu, Athi River, and Kitengela are the most affordable mortgage market with a household requiring a median income of between Kshs 150,000 – Kshs 300,000 to purchase a house using a mortgage, whereas, Roselyn, Karen, Runda, Muthaiga, and Kitusuru are the most unaffordable mortgage markets with households requiring a minimum monthly income of Kshs 1 Million to purchase a house using a mortgage (Cytonn Research, 2017) In summary for one to be able to afford the cheapest mortgage in the Nair obi Metropolitan area, his qualifying income has to be higher than Kshs 100,000. Many households in need of housing do not earn this kind of money. The average Kenyan household earns a monthly salary of Sh53,733. Very few people earn REAL ESTATE, 2018 23 high salaries, as most of the workers earn much lower salaries (Kenya National Bureau of Statistics, 2016). At least every single household has one form of credit/debt. The impact such debts/credit have on one’s ability to take or qualify for a mortgage will depend on the significance of total debts taken on the qualifying income to take a mortgage. The money that is left to go into a mortgage for a home is what can be used to determine the level of impact of other debts on the qualifying income, for capable households. REAL ESTATE, 2018 24 2.6 Conceptual Framework Household Debt • Types of household debts • Why households have debts? • How household debt can be a problem? Household Over-indebtedness • Measure of Overindebtedness • Impact on mortgage qualification Mortgage Accessibility Source: Author, 2018 REAL ESTATE, 2018 25 CHAPTER THREE RESEARCH METHODOLOGY AND BACKGROUND OF CASE STUDY 3.1 Introduction This chapter paints out the layout of the research framework. It outlines and explains the techniques adopted for research, the study area, research tools, and data collection methods, data analysis and the sampling method. This section concludes by covering the data presentation methods and ethical issues of research. 3.2 Area of Study The area of study for this research is in the general Nairobi area and its’ Metropolitan Region. These two possess the best attributes that will display a clear distinction between household indebtedness and mortgage qualification. The study will focus on households registered by the Institution of Surveyors of Kenya (ISK) as living or working in these regions. 3.2.1 Nairobi and Its Metropolis. Nairobi County is the country’s capital and largest city with an area of 696km2 (United Nations, 2018). The county was founded in the year 2013 on the same boundaries as the previous Nairobi Province. Its history dates back to the construction of the KenyaUganda Railway as early as the year 1896. It is Kenya’s most populated county based on the last official census in 2009. At the time the population was 3,138,369 in the city proper, the number has since grown to approximately 3.5 million (United Nations, 2018). The original name of the area was “Ewaso Nai’beri”, a Maasai name meaning “a place of cool waters”. It is believed the British had a hard time pronouncing the word and therefore, they coined their own name, calling it “Nairobi”. Currently, Nairobi is found within the Greater Nairobi Metropolitan region, which consists of 5 out of 47 counties in Kenya. These counties are Nairobi county, Kiambu county, Murang’a county, Kajiado county, and Machakos county. REAL ESTATE, 2018 26 Figure 3.1: A Satellite map of Nairobi. Source: Maps of World Website. The general Nairobi area is governed by zoning regulations that control its land use patterns. The various regional nodes based on the zoning regulations and locations are; 1. Commercial Zones: These areas include Nairobi CBD, Kilimani, Westlands, Riverside, and Upperhill, they are characterized by commercial office buildings, 2. High Rise Residential Areas: These are areas characterized by high rise residential developments mainly apartments and include: Kileleshwa, Dagoretti, Ridgeways, Githurai, Embakasi, Kahawa and Kasarani, 3. Low Rise Residential Areas: These are areas zoned for low rise residential developments, mainly villas, townhouses, and mansionettes and include; Kitisuru, Runda, Nyari, Karen and Spring Valley, 4. Satellite Towns: Land in the area is categorized into serviced (site and service schemes) and unserviced land. (Cytonn Research, 2017) REAL ESTATE, 2018 27 3.2.2 The Institution of Surveyors of Kenya (ISK) The Institution of Surveyors of Kenya is the professional organization in this country that is mandated to bring together and register individuals specializing in the discipline of the surveying profession namely the Valuers, Land Surveyors, Geomatic Engineers, Registered Estate Agents, Property Managers, Building Surveyors, Land Administration Managers, and Facilities Managers. The Institution of Surveyors of Kenya was inaugurated on 17th April 1969 and subsequently registered on 12th August 1969 as a body corporate under the Societies Act. Its members are engaged in both the public and private sectors (ISK, 2018). 3.3 Research Design A research design is a planned procedure adopted by the researcher in an attempt to answer questions in a valid, objective, accurate and economical manner (Kumar, 2005). Research design functions to assist in the identification and development of procedure and more importantly, in logistical arrangements which are required to undertake a research study. Information was obtained from registered professionals in various survey chapters in ISK. Due to the large number of households in Nairobi (the exact number is unknown), the researcher had to ensure that the study was conducted in an economical manner while still remaining objective as possible. This was achieved by the researcher narrowing down his target population to only registered Full Members in the various chapters of ISK. This is based on the criterion that for individuals to be registered as Full Members, they need to have been in professional practice for some years, and the assumption that they have advanced on, in experience, and financial well-being. This makes them suitable candidates to be examined on the relationship between over-indebtedness and mortgage qualification. REAL ESTATE, 2018 28 3.3.1 Target population and sample size A target population is the composition of all persons, things that the researcher can sensibly take a broad view of his discovery to. (Mugenda, 2008). In this case, the target population was 703 Fully registered Members of ISK. Sampling is a process of selecting a number of individuals for a study in such a manner that the selected individuals represent a larger group from which they were selected. The sample size was determined using the following formula: 𝑛= 𝑍 2 𝑝𝑞𝑁 𝑒 2 (𝑁 − 1) + 𝑍 2 𝑝𝑞 Source (Chava and Nachmias, 1996) Whereby: n = sample size N = Size of population p = proportion estimated to be having the factors being measured. (Assume a 95% confidence level of the population) q = (1-p) which is 0.05 e = confidence interval usually taken as 0.05 Z = the standard normal deviate at the required confidence level (Z=1.96) The total population of the study is 703, therefore N = 703 The sample n can then be calculated as follows: 𝑛= (1.962 -) (0.05)2 (703 − 1) + (1.96)2 (0.95)(0.05) n = 66 Hence a total of 66 questionnaires were administered using random and purposive sampling techniques of data collection. REAL ESTATE, 2018 29 3.3.3 Sampling technique • Purposive sampling It is a form of non-probability sampling that coincides to a particular criterion (Cooper and Schindler, 2006). Therefore, respondents that met the required characteristics were purposely selected for the research study. • Random sampling Respondents meeting the required criterion were selected at random giving each person an equal opportunity of being selected. 3.4 Data collection The study aimed to acquire information by enquiring on persons about their perception, their ideals, and point of view. In order to achieve these, materials of data collection are used. The tools used during the study include pens, notebooks, laptop, and structured questionnaires. The study will rely on two sources of data collection, the primary and secondary sources of data. 3.4.1 Primary data This is data collected from all responsible and related parties. These are mainly done through; • Questionnaires • Oral interviews 1. Questionnaires The questionnaires were prepared and distributed to respondents fulfilling the sample choice characteristics. The questionnaires administered were used to collect information directly from the sampled respondents and consisted of both open and closed-ended questions. The open-ended questions provided the respondents with an opportunity to freely express their views resulting in a greater variety of information. The closed-ended questions required yes or no answers. The questionnaires were administered both in person and through online emailing. Hence the questionnaires had to be designed with active checkboxes where the respondents REAL ESTATE, 2018 30 could click to activate and show their choice of answers. These questionnaires were sent and received back from respondents via email through the internet. 2. Oral interviews This refers to the direct communication between the researcher and respondent. The interviews were structured and the pen and notebook were used to record the data. The researcher adopted the use of structured interview schedules designed to help direct the interview so that respondents could have the chance and freedom to explain their answers. 3.4.2 Secondary data This was obtained through reviewing information from previous studies and articles, literature found in the library and the internet and from both published and unpublished reports connected to the research topic. Other sources of secondary data for these research include companies’ webpages and websites belonging to government institutions and property firms. The laptop was used to compile these research project and perform complex mathematical processes of data analysis and representation. 3.5 Data Analysis and Presentation The data that was obtained by the researcher from the study was sorted out into qualitative and quantitative data. The quantitative data collected was assigned numerical values for easier analysis. The data was further analyzed using descriptive statistics and presented using tables, charts, and graphs, to enable the researcher to meaningfully describe the distribution of scores. Qualitative responses gathered from administered questionnaires were categorized and had numbers assigned to them. The data was analyzed using the IBM Statistical Package for Social Sciences version 22 for data analysis. The research was mainly guided by the objectives of the study as listed in chapter one. All these were done with the intention to come up with relevant recommendations to improve homeownership (owner-occupancy) in Kenya. REAL ESTATE, 2018 31 3.6 Ethical considerations The researcher obtained a written permit to collect data from the appropriate respondents. This included a letter from the department of real estate and construction management at the University of Nairobi authorizing the researcher to carry out the research. The letter also functioned as an introductory request letter that was addressed to the respondents for courtesy purposes. REAL ESTATE, 2018 32 Table 3.1: Summary Table of Research Methodology SOURCES DATA Questionnaires METHODS COLLECTION analysis Excel METHOD ANALYSIS OUTPUT DATA DATA NEEDS Households Interviews OBJECTIVES Type of loans sourced. (ISK registered Graphs Tables Identify Number of loans sourced Charts worksheet sources/types of IBM SPSS version Questionnaires analysis Excel Full Members) Households Interviews credit and loans Tables Percentage of income spent on both (ISK registered 22 worksheet Measure for secured & unsecured payments. Graphs household over- Full Members) Charts Effect on fulfilling consumer needs Households Interviews Questionnaires analysis Excel 22 IBM SPSS version indebtedness Arrears Tables Percentage of income left to go into (ISK registered Number of credit commitment worksheet Impact of overa mortgage. Graphs indebtedness on Full Members) Charts Effect on personal credit rating & IBM SPSS version mortgage uptake Questionnaires Excel 22 Households debt to income ratio Tables Number of households that would analysis Charts Graphs worksheet Establish Interviews 22 IBM SPSS version (ISK registered Full Members) take a mortgage given their current income and credit commitments mortgage affordability of households. 33 REAL ESTATE, 2018 CHAPTER FOUR DATA ANALYSIS AND INTERPRETATION 4.1 Introduction This research was an attempt to investigate the impact of households’ over-indebtedness on mortgage qualification for homeownership, with a case study of Fully registered ISK surveyors practicing in and around the Nairobi Area. The case study was chosen with the assumption that all respondents live and work in Nairobi and its general Metropolitan area, hence satisfying the major factors that would impact on the study findings. The study’s findings will form the basis of the analysis and presentation which will enable the researcher to arrive at suitable conclusions and recommendations. Simple descriptive statistics such as the mean, tables and graphs have been used to display, describe and represent the research findings through the organization of the raw data into some purposeful and usable categories. Qualitative data collected from respondents has been presented as narratives while the use of tables is preferred since it summarizes data in an orderly manner and conserves on space and time, as it reduces the explanatory clauses to the minimum. The respondents were asked questions through the use of questionnaires supported by unstructured interviews to help get adequate information from all respondents. The questionnaires were administered using two modes. Firstly, the researcher personally administered the questionnaires to respondents and secondly, the researcher opted to administer the questionnaires through online emailing by using the internet. The former method proved more effective since the personal follow up of the researcher moved the respondents to be more co-operative. The researcher collected data on the respondents’ socioeconomic characteristics, the kind and number of credit commitments they had, the monthly income they spent on both secured and unsecured payments and if they had any arrears. The respondents were also asked if their credit commitments infringed on their ability to meet their basic needs and if they considered their debts a heavy burden. Finally, the respondents were asked if they would consider taking a mortgage at the time while considering their own credit commitments and income. The researcher asked for recommendations on how mortgage could be made more affordable to everyone. REAL ESTATE, 2018 34 4.2 Analysis of Questionnaires Response The response rate to the questionnaires administered to the respondents was analyzed and presented as follows. A total of 66 questionnaires were administered to various respondents. Out of the sixty-six questionnaires issued, 46 questionnaires were administered personally while 20 of the remaining questionnaires were administered through online emailing using the internet. 39 questionnaires out of the 46 questionnaires administered personally were successfully collected by the researcher and deemed usable for research, this mode of administering questionnaires had an (85%) success rating. Out of the 20 questionnaires administered using online emailing, 6 questionnaires were received back, indicating a (30%) success rating. In total, 45 questionnaires out of the 66 questionnaires printed for field study were filled by respondents, giving a total success rating of (68%), which is adequate for research and generalization purposes. The data is analyzed and presented in table 4.1 and graph 4.1 below. Table 4.1 Rate of response to the questionnaires administered Mode of Number Received Percentage response rate (%) administering issued Personally 46 39 85% Online Emailing 20 6 30% TOTAL 66 45 68% Source: Primary Data. While administering questionnaires, a response rate of 50 percent is adequate for analysis and reporting, 60 percent is a good response while 70 percent is very good (Mugenda & Mugenda, 1999). Therefore, the response rate in this research was good for analysis and presentation. REAL ESTATE, 2018 35 Graph 4.1 Rate of response to questionnaires administered RATE OF RESPONSE TO ALL QUESTIONNAIRES ADMINISTERED No response 32% Response 68% Source: Primary Data. 4.3 Respondents’ gender. The sample comprised of 28 male respondents (62%) and 17 female respondents (38%) as analyzed and presented in table 4.2 and graph 4.2 below. Table 4.2: Gender Distribution Gender Number of respondents Percentage (%) Male 28 62% Female 17 38% TOTAL 45 100% Source: Primary Data. REAL ESTATE, 2018 36 Graph 4.2: Respondents gender distribution GENDER DISTRIBUTION Female 38% Male 62% Source: Primary Data. 4.4 Respondents’ marital status A large number of the respondents, 28 in number identified themselves as being single. This represented (62%) of the sample population while 17 respondents were in a marriage relationship. This represented (38%) of the sample population. None of the respondents identified themselves as being widowed or divorced. Table 4.3 Respondents’ marital status Marital Status Number of respondents Percentage (%) Single 28 62% Married 17 38% Widowed 0 --- Divorced 0 --- TOTAL 45 100% Source: Primary Data REAL ESTATE, 2018 37 Graph 4.3 Respondents’ marital status Respondents Marital Status 30 62% 25 20 38% 15 10 5 0 0 Widowed Divorced 0 Single Married Respondents Marital Status Source: Primary Data. 4.5 Respondents’ level of education. The sample comprised of a group of well-educated professionals seeing that they are fully registered members of the Institution of Surveyors of Kenya. 15 respondents (33%) had a University’s degree in their field of study, 25 of them (56%) had masters’ degrees while 5 of them (11%) had accomplished their doctorate degree studies. All respondents had surpassed the secondary and tertiary education levels. Table 4.4 Respondents level of education Level of education Number of respondents Percentage Secondary 0 --- Tertiary 0 --- Degree 15 33% Masters 25 56% PhD/Doctorate 5 11% TOTAL 45 100% Source: Primary Data. REAL ESTATE, 2018 38 Graph 4.4 Respondents level of education Number of respondents PhD/Doctorate 0% 11% Degree 33% Secondary Tertiary Degree Masters 56% Masters PhD/Doctorate Source: Primary Data. 4.6 Respondents’ employment status. A total of 43 respondents (95.5%) were employed and practicing in either the private sector or the public sector. 24 of them (56%) were in practice in the private sector while 19 of them (44%) were employed in the public sector. A total of 2 respondents (4.5%) were self-employed and indicated having their own firms. Table 4.5: Respondents’ employment status Employment Details Number Status of Percentage (%) respondents Employed Private 24 Sector Public 19 43 95.5% Sector Self Employed Own Firms 2 4.5 % Unemployed ------ 0 ---- Other ------ 0 ---- Source: Primary Data. REAL ESTATE, 2018 39 Graph 4.5: Respondents’ employment status. 30 25 24 19 20 15 10 5 2 0 0 Employed Self Employed Private Sector 24 2 Public Sector 19 Unemployed Other 0 Source: Primary Data. 4.7 Respondents age bracket. The table below gives the distribution of households age bracket as recorded by individuals. The majority of the sample population averaged between age brackets (20 – 30) and (30 – 40). With 46.7 percent in their mid-twenties and 48.9% in their mid-thirties. The two respondents in their forties were self-employed and had their own firms. Table 4.6 Respondents age bracket. Age Group Number of respondents Percentage (20 – 30) 21 46.7% (30 – 40) 22 48.9% (40 – 50) 2 4.4% (Above 50) 0 ---- 45 100% TOTAL Source: Primary Data. REAL ESTATE, 2018 40 Graph 4.6 Respondents age bracket. Number of respondents (40 – 50) 4% (Above 50) 0% (20 – 30) 47% (30 – 40) 49% (20 – 30) (30 – 40) (40 – 50) (Above 50) Source: Primary Data. 4.8 Households Income Bracket. The households’ income bracket had an even distribution. Households’ earning between (10,000 – 25,000) were 11 in number, accounting for (24.5%) of the sample population. The income bracket (25,000 – 40,000) had the highest number of households, 15 in total, and accounting for (33.3%) of the sample population. The income bracket (40,000 – 55,000) had 12 households (26.7%) while income brackets (55,000 – 75,000) and (75,000 – 100,000) had the least number of households, 5 and 2 households respectively. No households of the sample population indicated earning an income above Kshs. 100,000. The level of households’ income determines the ability to afford a mortgage and thus considered important. REAL ESTATE, 2018 41 Table 4.7 Households Income Bracket. Income Bracket Frequency of response Percentage (10,000 – 25,000) 11 24.5% (25,000 – 40,000) 15 33.3% (40,000 – 55,000) 12 26.7% (55,000 – 75,000) 5 11.1% (75,000 – 100,000) 2 4.4% (Above 100,000) 0 ----- TOTAL 45 100% Source: Primary Data. Graph 4.7 Households Income Bracket. Households' Distribution of Income 40.00% 33.30% Percentage of population 35.00% 30.00% 25.00% 26.60% 24.50% 20.00% 15.00% 11.10% 10.00% 4.40% 5.00% 0.00% -5.00% (10,000 – 25,000) (25,000 – 40,000) -10.00% (40,000 – 55,000) (55,000 – 75,000) (75,000 – 100,000) (Above 100,000) Income Bracket Household Source: Primary Data. The above analysis paints a grim picture since the percentage of households that are shown to be capable of affording a mortgage comfortably is only 4.40% with 11.10% of the population closely following. REAL ESTATE, 2018 42 4.9 Response to the existence of a credit commitment/loan A great number of households, 36 in number (80%), indicated having taken at least one form of credit commitment. Only 9 households (20%) indicated not having any debts. The findings based on the data collected, support the statement that households have a tendency of borrowing so as to maintain a steady living condition (D’Alessio & Iezzi, 2012). Table 4.8 Response to the existence of a credit commitment/loan Type of response Number of respondents Percentage Yes 36 80% No 9 20% TOTAL 45 100% Source: Primary Data. Graph 4.8 Response to the existence of a credit commitment/loan Household under credit commitment 20% 80% Yes No Source: Primary Data. The high number of households taking on credit and loans shows that credit availability is high hence they can borrow today and pay in future, especially when they do not have the upfront money to meet their needs. REAL ESTATE, 2018 43 Table 4.8.1 Response to the type of credit/loan taken Households credit sources varied from one household to another. Mobile money loans were the most popular source of credit and loans while mortgage loans were the least sourced loans. Type of Credit/Loan Frequency of response Percentage Mortgage 5 11.11% Student Loan 23 51.11% Credit Cards 8 17.78% Micro Finance Loans 25 55.56% Shylocks 12 26.67% Advance Airtime 32 71.11% Car Loans 7 15.56% Advance Salary Loan 18 40.00% Co-operative Loan 11 24.44% Chama Loan 24 53.33% Mobile Money Loan 37 82.22% Other 8 17.78% Source: Primary Data. Graph 4.8.1 Response to type of credit/loan taken. Sourcing rate 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 82.22% 71.11% 55.56% 51.11% 53.33% 40.00% 11.11% 15.56% 17.78% 17.78% REAL ESTATE, 2018 24.44% 26.67% 44 The data collected, on types of credit/loans sourced, and analyzed in percentages are in support with the study’s main theme of concern. Findings based on data was that mortgage loans were least sourced with many households indicating that low incomes and high interests playing a significant role in low mortgage uptake. The researcher did inquire why a great number of responses leaned on mobile money loans. Respondents pointed out the convenience and ease of mobile money loan sourcing. The study, therefore, draws a conclusion that ease of credit sourcing plays an important role in how the numbers have been distributed in the graph and table above. Table 4.8.2 Response to total number of loans/credit taken. Number of loans/credit Frequency of response Percentage 1 7 19.44% 2 7 19.44% 3 10 27.78% 4 8 22.22% Above 4 4 11.11% 36 100% TOTAL Source: Primary Data. Graph 4.8.2 Response to total number of loans/credit taken. Frequency Percentage of populations number of credits & loans 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% 27.78% 19.44% 22.22% 19.44% 11.11% Percentage 1 2 3 4 Above 4 Number of Loans Source: Primary Data. The analysis shows that (27.78%) are close to being over-indebted whereas (22.2%) of the sampled households are already over-indebted. A conclusion is drawn from the over- REAL ESTATE, 2018 45 indebtedness indicator that states, households having up to 4 credit commitments are measured as being over-indebted. 4.10 Response to spending more than 30% of monthly income on repayments. Out of the 36 respondents who had one or more forms of credit commitments, 29 of them (80.5%) indicated that they spend more than 30 percent of their monthly income on credit commitments whereas 7 of them (19.5%) did not. The 29 individuals meet the second measure and indicator of over-indebtedness, that considers households spending more than 30 percent of their monthly income on repayments as being over-indebted. Table 4.9: Response to spending more than 30% of monthly income on repayments. Type of response Number of respondents Percentage Yes 29 80.5% No 7 19.5% 36 100% TOTAL Source: Primary Data. Graph 4.9. Response to spending more than 30% of monthly income on repayments. Response 20% 80% Yes No Source: Primary Data. REAL ESTATE, 2018 46 4.11 Response to credit repayments infringing on the ability to fulfill needs. The third indicator of being over-indebted is if your total repayments take you below the poverty line. To ascertain this the researcher asked respondents if their total credit commitments hindered them from fulfilling their basic needs. Only 2 households indicated that they could not meet their needs as they would wish. This represents a very low number and is therefore considered statistically insignificant. Table 4.10 Response to credit repayments infringing on the ability to fulfill needs. Type of response Number of respondents Percentage Yes 2 5% No 34 95% TOTAL 100% Source: Primary Data. Graph 4.10 Response to credit repayments infringing on the ability to fulfill needs. Response 5% 95% Yes No Source: Primary Data. REAL ESTATE, 2018 47 4.12 Response to being 2 months in arrears on credit commitments or household bill. Arrears arise from the aspect of households’ incomes being insufficient to meet both the household bills or commitments. Very few households, 4 in number (11%), indicated being in arrears on their repayments while most indicated not being in arrears. Therefore 89 percent of the households are not to be considered as being over-indebted when using the arrears indicator to measure for over-indebtedness. Table 4.11 Response to being 2 months in arrears on credit commitments or household bill. Type of response Number of respondents Percentage Yes 4 11% No 32 89% TOTAL 36 100% Source: Primary Data. Graph 4.11 Response to being 2 months in arrears on credit commitments or household bill. Number of respondents 11% Yes 89% No Source: Primary Data. REAL ESTATE, 2018 48 4.13 Response to considering credit commitments a “heavy burden” The individual’s perception of debt and credit commitments is usually considered as the last measure for over-indebtedness. This is because it is a more subjective measure for over-indebtedness compared with the other indicators. Table 4.12: Response to considering credit commitments a “heavy burden” Type of response Number of respondents Percentage Yes 7 19.5% No 29 80.5% TOTAL 36 100% Source: Primary Data. Households perceiving their credit commitments as being a heavy burden showed trends of having more than 3 credit commitments. 4.14 Response to the effect of personal credit commitments on Credit Rating and Debt-to-Income-ratio. Half of the respondents (50%), with credit commitments (18 of 36), indicated, that, their current debts are likely to only affect their credit rating when being considered for a mortgage while 22 percent indicated that only their debt-to-income ratio will be affected. Therefore, this indicated that most households were under-informed on the impacts of credit commitments on both the credit rating and debt to income ratio. The findings should have supported the fact that debts taken affect both the credit rating of individuals and also their debt-to-income ratio. The study’s findings can, therefore, conclude that 28 percent of the respondents were better informed on debt impacts REAL ESTATE, 2018 49 Table 4.13: Response to the effect of personal credit commitments on Credit Rating and Debt-to-Income-ratio. Type of response Number of respondents Percentage Credit Rating 18 50% Debt-to-Income-ratio. 8 22% Both 10 28% TOTAL 36 100% Source: Primary Data. 4.15 Response to considering taking a mortgage while considering their credit commitment and income 38 out of 45 households would not consider taking a mortgage while citing reasons of low income and high interest rates charged on mortgages. 7 of the respondents (15%) indicated that they would consider taking a mortgage with a most of them inkling to lower mortgage amounts. Table 4.14 Response to choosing to take a mortgage while considering their credit commitment and income. Type of response Number of respondents Percentage Yes 7 15% No 38 85% TOTAL 45 100% Table 4.13.1 Response to mortgage amount considered Loan Amount Number of respondents Percentage (2M – 5M) 1 14.3% (5M – 10M) 3 28.9% (10M – 20M) 2 28.2% (20M – 30M) 1 14.3% (30M – 40M) -------- --------- (Above 40M) 1 14.3% TOTAL 7 100% REAL ESTATE, 2018 50 3.15 Challenges Encountered Some of the problems and challenges encountered during the study were that the researcher got a 68 percent response rate from the respondents and not 100%. This affected the analysis since the sample was reduced. The reason for this was that some respondents were too busy and could not get time to fill the questionnaires or grant interviews. However, according to Mugenda (1999), a response rate of 60% and above is a good response. The findings of this study are therefore justified. The other main problem the researcher faced was a great deal of difficulty to get the respondents to divulge or offer the required information. Some respondents declined to answer some questions which they felt were touching on sensitive areas, for instance on their job security and family expenditure. In addition, some respondents refused to fill the questionnaires completely and the researchers’ effort to get these respondents to fill the questionnaires, made these respondents rather aggressive. Those who agreed after a lot of persuasions ended up giving stereotyped answers that were too general. However, the literature reviewed and answers from other honest respondents were used to assess the truthfulness of the responses. Finally, the limitation of time and finances also posed a great challenge during fieldwork. REAL ESTATE, 2018 51 CHAPTER FIVE CONCLUSIONS AND RECOMMENDATIONS 5.1 Introduction This chapter contains an overview of the research study, insights gained from the research and recommendations for further research studies. Revisiting the research objectives The main aim of the study was to: investigate the impact of households’ overindebtedness on mortgage qualification for home ownership. It focused on the households’ credits and loans sourcing habits, by identifying the types of loans/credits they had taken or are currently having and then establishing if their borrowing had driven them to a state of over-indebtedness. The final objectives were to establish the impact of being over-indebted on mortgage uptake and to identify measures to improve the level of mortgage uptake based on study findings. 5.2 Emerging themes. This study followed a guideline of tracing households’ borrowing habits and how it finally affects them being able to qualify for a mortgage uptake, despite taking this perspective, one theme emerged from the research. a. Poor mortgage indoctrination; where households have yet to change their critic beliefs on financing house purchases using mortgages. 5.3 Implication of findings on the hypothesis The hypothesis for this study states that: III. Null hypothesis (H0): Households’ over-indebtedness does not affect mortgage qualification. IV. Alternative hypothesis (H1): Households’ over-indebtedness affects mortgage qualification. REAL ESTATE, 2018 52 The data collected in this study supports the adoption of the Alternative hypothesis (H1). With (85%) percent of the households indicating that they would not take a mortgage, in due consideration of their income and credit commitments, the number is considered statistically significant to point to the general trend of mortgage uptake in relation to household debts. 5.4 Conclusions The conclusions below are presented in relation to the study objectives and hypothesis described previously: i. What are the various sources and types of household debts a modern consumer faces? The availability and ease of access to various credit and loan options has made it a possibility for any kind of household to get a credit/loan, ranging from microfinance loans to mobile loans. This has increased the number of indebted households in the country. This indicated by the fact that all households indicated having at least one credit commitment. Mortgage loans are the least accessed by households given their strict eligibility criteria and low income levels of households. Furthermore, the ease of access of other loan options like mobile money loans have captured the majority of the households’ populations and opened new windows of opportunities for them that they cannot get from mortgage loans. ii. How to determine households’ over-indebtedness? There are various indicators of over-indebtedness as previously mentioned in the Literature review. This include: ⎯ making high repayments relative to income, ⎯ being in arrears, ⎯ making heavy use of credit and ⎯ finding debt, a burden REAL ESTATE, 2018 53 The study concluded that a majority of the households did satisfy at least one to two criteria of determining over-indebtedness, hence indicating proof of over-indebted households. iii. What is the impact of households’ over-indebtedness on mortgage uptake? The study findings help to arrive at the conclusion that being over-indebted greatly affects households credit rating, and would inevitably affect their mortgage qualification. Most households are unaware of how their debt-to-income ratio could affect their possibility of financing a mortgage loan. This is supported by the fact that very few households considered indicating that their debts would, in fact, affect their debt-toincome ratio score. iv. How affordable is a loan mortgage among the various households? The major impact of households being over-indebted is the reduction of spendable income once current credit commitments have been met. Majority of the households hence opted not to take a mortgage in such case. This is in due consideration of their income and existing credit commitments. Hence mortgage loans will continue being unaffordable for such households. 5.5 Recommendations From the conclusions, the following recommendations are offered; i. There is a need for governing bodies, like CBK, ensures lending institutions review their individual-based credit lending processes and start campaigning on smart lending and borrowing. These would work to reduce the total sum of unnecessary consumer credit commitments that impact negatively on households’ credit ratings. ii. Both lending institutions and households should approach a preventive approach to over-indebtedness, by encouraging households to save more. Savings would work to buttress emergency spending and support worthwhile investments like home purchases and starting a business. iii. Mortgage lending institutions should tailor mortgage loans to be more lucrative to households. Since mortgages are still expensive due to high property prices, local lenders should consider borrowing a leaf from other countries that offer REAL ESTATE, 2018 54 multi-generational mortgages. This offers a chance for home buyers to share the cost of paying for a house with the future generation. iv. Lending institution should conduct educative programs on mortgage financing to improve its indoctrination to the Kenyan culture while clearing the obscurity that is often associated with mortgage lending. This will help sort out transparency issues and work to improve mortgage uptake. 5.6 Areas of further study. In order to fully understand the different and various factors that affect mortgage uptake in Kenya, including over-indebtedness of households, it would be prudent to focus further research on: 1. The Poor Indoctrination of Mortgage Financing and its Effect on Mortgage Uptake. (A Case Study of Kenya). 2. Legal Fees on Mortgage Financing and How They Eventually Affect Mortgage Interest Rates. 3. Mortgage Insurance and How It Eventually Affects Mortgage Interest Rates REAL ESTATE, 2018 55 Bibliography Aspen Institute. (2018, August 28). Consumer Debt: Aspen Institute. Retrieved from The Aspen Institute: https://www.aspeninstitute.org/tag/consumer-debt/ Bank of Africa. 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Franco Modigliani and the Life Cycle Theory of Consumption. Rome: Princeton University. Retrieved from https://www.princeton.edu/~deaton/downloads/romelecture.pdf DICKERSON, M. (2010). Consumer Over-Indebtedness: A U.S. Perspective. International Law, 24. European Commission. (2008a). Towards a common operational European definition of overindebtedness. European Commission. (2010). Over-indebtedness: New evidence from the EU-SILC special. Gale, T. (2008). Economics, Personal Money Management, and Entrepreneurship. Everyday Finance. REAL ESTATE, 2018 56 Haas, O. J. (2006). Over-indebtedness in Germany. Social Finance Program Working Paper No. 44. Harari, D. (2018, May 10). Household debt: statistics and impact in the economy. House Of Commons Library, Briefing Paper Number 7584, p. 26. In the Cambridge Dictionary. (2018). Investopedia. (2018). Microfinance. Retrieved from Investopedia, LLC. ISK. (2018). About US: Institution of Surveyors of Kenya. 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Retrieved from World Population Review: http://worldpopulationreview.com/world-cities/nairobi-population/ REAL ESTATE, 2018 58 APPENDIX: QUESTIONNAIRE TO THE HOUSEHOLDS I am Samwel Ondari Meja undertaking a research project paper on households’ overindebtedness and the impact it has on mortgage qualification for home ownership in partial fulfillment for the Award of a Bachelor’s Degree in Real estate in the school of the Built Environment, Department of Real Estate and Construction Management in the University of Nairobi. This study is for academic purpose only and any data or information shall be treated with great confidence. Kindly take some time to answer the following questions honestly to assist me to complete my study. Thank you. Date ………………………………… Questionnaire number ………………………. Section I – Socio-economic characteristics of the respondent 1. Respondent’s Name (Optional) ……………………………………………………… 2. Gender Male ☐ Female ☐ 3. Marital Status Single ☐ Married ☐ Widowed ☐ Divorced ☐ 4. Level of education Secondary ☐ Tertiary ☐ Degree ☐ Masters ☐ Ph.D./Doctorate ☐ Other☐Specify……………………………………………………………… 5. Employment status Employed (Private Sector) ☐ Employed (Public Sector) ☐ Self-employed ☐ Unemployed ☐ Other☐Specify……………………………………………………………… REAL ESTATE, 2018 59 6. Name of company/organization you work with ………………………………………………………………………………………… 7. Age Bracket (20 – 30) ☐ (30 – 40) ☐ (40 – 50) ☐ (Above 50) ☐ 8. Household Income Bracket (10,000 – 25, 000) ☐ (25,000 – 40, 000) ☐ (40,000 – 55, 000) ☐ (55,000 – 75, 000) ☐ (75,000 – 100,000) ☐ (Above 100,000) ☐ 9. Are you under any type of credit commitments? Yes ☐ No ☐ If Yes, indicate which one(s) ☐Mortgages ☐Car loans ☐Student loans/School Fee Loan ☐Advanced Salary Loan ☐Credit Cards ☐Co-operative ☐Microfinance Loans ☐Chama Loans ☐Shylocks ☐Mobile Money Loans ☐Advance Airtime ☐Other (Specify) ………………………………………………………………………. 10. Total Number of loans/credit. ______________ REAL ESTATE, 2018 60 11 Do you spend more than 30% of your gross monthly income on total borrowing repayments (both secured & unsecured). Yes ☐ No ☐ 12. Does your credit commitments infringe on and prevent the fulfillment of consumption needs? Yes ☐ No☐ 13. Have you been 2 months in arrears on credit commitments or household bill? Yes ☐ No ☐ 14. Do you consider your credit commitments a “heavy burden”? Yes ☐ No ☐ 15. Which of these two aspects, considered in mortgage qualification, would your debts (credit commitments) most likely to affect? ☐Credit rating/score (Establishes your creditworthiness) ☐Debt to income ratio (Percentage relation of debt to income) 16. Given your current income and credit commitments would you consider taking a mortgage for a house purchase? Yes ☐ No ☐ a) If Yes: Which Loan amount? (2M – 5M) ☐ (5M – 10M) ☐ (10M – 20M) ☐ (20M – 30M) ☐ (30M – 40M) ☐ (Above 40M) ☐ REAL ESTATE, 2018 61 b) If No: Give reason(s) for not taking a mortgage. …………………………………………………………………………………… …………………………………………………………………………………… ……………………………………………............................................................ ....................................................................................................................... 17. Give your recommendations on how a mortgage can be more affordable. …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………… REAL ESTATE, 2018 62