Keith Kiprop Kimeli

King County Housing Market Analysis: Final Report This document combines the final data story, design choices made based on audience needs, and a reflection on the original project proposal. Project Proposal: King County Housing Market Analysis 1. Topic and Justification Topic: King County Housing Market Analysis Justification: I am interested in analyzing the King County Housing Market to better understand the relationship between various housing features and their price points. This information can be valuable to potential homebuyers, sellers, and investors in the area. 2. Business Case and Objectives Business Case:  This project aims to provide insights into the King County housing market, including trends, property value drivers, and potential buying or selling opportunities.  The findings can be used by realtors to inform their clients and make data-driven recommendations.  Potential homebuyers can leverage this information to identify areas and property types that fit their budget and needs.  Investors can utilize the analysis to make informed decisions about buying and selling properties in King County. Objectives:  Identify trends in King County housing prices over time.  Analyze the relationship between house price and features like bedrooms, bathrooms, square footage, lot size, and waterfront location.  Explore how house condition and year built affect marketability.  Create a visual representation of the data to communicate insights effectively. 3. Persona Persona 1: Sarah, the Homebuyer  Age: 32  Occupation: Marketing Manager  Needs: Sarah and her husband are looking to buy their first home in King County. They are interested in a 3-bedroom house with at least 2 bathrooms in a safe neighborhood with good schools. They would prefer a house with a yard but are open to considering different options depending on their budget. Persona 2: David, the Real Estate Investor  Age: 45  Occupation: Real Estate Investor  Needs: David is looking to invest in properties in King County with the potential for high return on investment. He is interested in identifying undervalued properties that he can renovate and resell for a profit. 4. Data Description The data provided is a sample of King County housing sales. It includes the following attributes:  date: Sale date of the property  price: Sale price of the property  bedrooms: Number of bedrooms  bathrooms: Number of bathrooms  sqft_living: Square footage of living space  sqft_lot: Square footage of the lot  floors: Number of floors  waterfront: Whether the property is located on the waterfront.  view: View quality (Excellent, Good, Average, Fair, No View)  condition: Condition of the property (Poor-Worn out, Fair-Badly worn, Average, Good, Very Good)  grade: Overall grade of the property (based on a scoring system)  yr_built: Year the property was built.  zipcode: Zip code of the property location  lat: Latitude coordinate of the property location  long: Longitude coordinate of the property location This data is well-suited for my project because it allows me to analyze the relationship between various housing features and their sale prices. 5. Presentation I plan to present my findings in a visually appealing and informative way using charts, graphs, and maps.  Charts and graphs will be used to show trends over time, distributions of house prices and property features, and correlations between variables.  Maps will be used to visualize price trends and housing characteristics geographically across King County. 6. Challenges  The provided data may not be representative of the entire King County housing market.  A small sample size might lead to inaccurate conclusions.  There may be additional factors affecting house prices that are not included in the data set, such as school district ratings or proximity to amenities. I plan to address these challenges by acknowledging the limitations of the data and by mentioning the need for further analysis that considers a more comprehensive dataset. Data Story URL: https://public.tableau.com/app/profile/keith.kiprop.kimeli/viz/KingCountyHousingMarketA nalysisMulti-slide/NavigatingKingCountyHousing?publish=yes Design Decisions Based on Audience Needs The original project proposal outlined a plan to analyze the King County Housing Market and present the findings in a way that benefits both Sarah, the homebuyer, and David, the real estate investor. This final data story fulfills that objective by providing insights into price trends, the impact of features on price, and how factors like view and condition can influence value. While the proposal planned for exploring school district ratings and proximity to amenities, these aspects were not included in the final data story due to data limitations. However, the focus on core features and their price impact remains valuable for both buyer and investor personas. Here are three specific design choices made with the audience in mind: 1. Dual-Persona Narrative: The introduction and conclusion specifically mention Sarah and David by name, highlighting how the data story caters to their distinct needs. This personalized approach keeps the audience engaged and clarifies how the information can be applied to their goals (buying a home vs. identifying investment opportunities). 2. Clear and Concise Visualizations: The data story utilizes a variety of charts and graphs, but ensures they are clear and easy to understand. Complex visualizations can be overwhelming, so using well-labeled charts with limited text allows both Sarah and David to grasp the key takeaways without getting bogged down in technical details. 3. Actionable Insights: The data story goes beyond just presenting findings. It translates those findings into actionable insights. For example, the heatmap on view and condition not only reveals the impact on price but also suggests potential renovation opportunities for David, the investor. Similarly, discussions on price ranges and bedroom distribution provide practical considerations for Sarah, the homebuyer. By incorporating these design elements, the data story effectively bridges the gap between data and practical application, making the information useful for both Sarah and David in navigating the King County housing market.