ADTA 5130 Section 400 – Data Analytics I

Executive Summary In this project, we analyzed how key factors impact on the housing price using a dataset with 20,437 observations and 17 variables including the property characteristics such as size, number of bedrooms and bathrooms, condition and location. The purpose was to find features that can express significant relationships and patterns in the data so that they can be of help to the stakeholders in the real estate industry. The study then used statistical analysis, including ANOVA, regression modeling, to determine critical housing price determinants and assess their impact. We find that it depends a great deal on the number of floors, property condition, and the quality of the view on home prices. The homes with more floors and with better view sold for much higher prices than those in conditions with lower value. Further, there was a positive correlation between the number of bedrooms and bathrooms, such that larger homes have greater number of bedrooms and bathrooms conveyed proportionally. In addition, newer structures are more likely to have more floors, mirroring changing buyer preferences. However, these insights suggest that developers should concentrate on constructing multi-story homes having scenic views and contemporary setups, while renovation should orient on improving property conditions to improve the market value. The findings offer insights on real estate development, marketing and investment strategies. Business Understanding / Analytics Question The main problem addressed by this project is to identify and understand the factors that have a great consequence on the price of houses in real estate market. Being a largely competitive and dynamic industry, the real estate industry requires that its key stakeholders; the developers, investors, and realtors be able to make informed decisions with data driven insights. Using a comprehensive dataset of property characteristics to identify patterns, it serves to elucidate ways in which pricing strategies, property development, and investment decisions can be informed. To achieve this, the analysis was guided by the following key analytics questions: 1. How does the number of floors affect housing prices? 2. How does the quality of the view affect the price of a property? 3. Does the number of bedrooms correlate with the number of bathrooms within a property? 4. How does the year the property was built relate to its design aspects, such as the number of floors? 5. Does the condition of the property really affect the price? Understanding these questions is crucial for stakeholders to ascertain the value-added aspects that are wanted on properties and to accordingly modify strategies. Answering these questions, the study provides practical data about market trends and consumer preferences to enable stakeholders to fine-tune their offerings to remain competitive within the real estate market. Data Understanding The dataset employed for this analysis contains 20,437 observations having 17 variables that provide rich information about housing characteristics based on physical aspects, location, and sales. The dataset contains variables such as price, number of bathrooms and bedrooms, living area square footage, lot square footage, number of floors, waterfront presence, view rating, property condition, grade, and year built/renovated. The dataset is an exhaustive source for the analysis about determinants of housing price. Some key variables that are relevant are: • Price: The target variable that indicates the selling price of every property. • Bathrooms and Bedrooms: Property size indicators and functional aspects that could influence buyer preferences. • Sqft_living: Interior space measurement. It has an effect on property price. • Floors and Waterfront: Features that generate unique property values, such as multistory buildings or water frontage. • View, Condition, and Grade: Characteristics that reflect the appearance and constructional nature of the property. • Year Built and Renovation Year: Property age and modernization indicators that can affect desirability and price