# RECORD c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6 # TITLE: Research on the Influencing Factors of House Prices in Chicago # YEAR: 2024.0 VENUE: Transactions on Economics, Business and Management Research DOI: 10.62051/1jq1vs36 # AUTHORS: Pin-Yao Liu # PAGES: 6 CANDIDATE TABLE PAGES (1-based): [4, 5] # ABSTRACT: This study provides a comprehensive analysis of the factors that influence housing prices in suburban Chicago. Using detailed regression analysis, several key variables were identified as significant predictors of property value. The study included variables such as the number of bedrooms, square footage, number of rooms, size of the lot, annual taxes, number of bathrooms, number of garages, and the condition of the house. The R-squared value of the model is 0.732, indicating that approximately 73.2% of the house price variance can be explained by the selected variable. Houses with more rooms and more square footage tend to cost more. In addition, characteristics such as the number of garages and bathrooms can also have a significant impact on house prices. These findings provide valuable insights for real estate professionals, buyers and sellers, enabling them to make informed decisions regarding real estate investment and pricing strategies. By understanding the key drivers of house prices, stakeholders can better navigate the suburban housing market and ensure more accurate assessments and strategic planning. ===== PAGE 1 (score 17, numeric-paren 0, table-words 1, var-words 15) ===== Transactions on Economics, Business and Management Research ISSN: 2960-1789, eISSN: 2960-2254 | Volume 10 MEEA 2024 Content from this work may be used under the terms of CC BY-NC 4.0 licence (https://creativecommons.org/licenses/by-nc/4.0/). Published by Warwick Evans Publishing. WEP Warwick Evans Publishing Research on the Influencing Factors of House Prices in Chicago Pinyao Liu* Business, University of Rochester, Rochester, 14627, The US * Corresponding Author Email: bulpy69@bu.edu Abstract. This study provides a comprehensive analysis of the factors that influence housing prices in suburban Chicago. Using detailed regression analysis, several key variables were identified as significant predictors of property value. The study included variables such as the number of bedrooms, square footage, number of rooms, size of the lot, annual taxes, number of bathrooms, number of garages, and the condition of the house. The R-squared value of the model is 0.732, indicating that approximately 73.2% of the house price variance can be explained by the selected variable. Houses with more rooms and more square footage tend to cost more. In addition, characteristics such as the number of garages and bathrooms can also have a significant impact on house prices. These findings provide valuable insights for real estate professionals, buyers and sellers, enabling them to make informed decisions regarding real estate investment and pricing strategies. By understanding the key drivers of house prices, stakeholders can better navigate the suburban housing market and ensure more accurate assessments and strategic planning. Keywords: House prices; Chicago suburbs; Real estate analysis; Regression model. 1. Introduction The analysis of home prices in suburban Chicago is an important area of study for understanding the dynamics of the real estate market. House prices are affected by a variety of factors, including but not limited to the size of the house, the number of bedrooms and bathrooms, lot size, annual taxes, the condition of the house, and the availability of amenities such as a garage. These variables interact in complex ways to determine the final price of the property. In this study, a dataset containing various characteristics associated with housing prices in suburban Chicago was used. The initial exploration, which included summary statistics and correlation analysis, revealed significant relationships between prices and several key variables. For example, the size of the house (Space) and the number of rooms (Room) show a strong positive correlation with price, indicating that the bigger the house, the more rooms, the more expensive the price [1]. In addition, the number of bathrooms and garages is also positively correlated with price, reflecting their importance in determining the value of a property [2]. To improve the predictive power of the model, polynomial features and interaction terms were combined. This approach allows for capturing nonlinear relationships and interactions between different variables. The polynomial regression model is significantly better than the linear model, explaining about 85.72% of the variance in house prices compared to 69.95% for the linear model [3]. Key interaction terms, such as the interaction between the number of bathrooms and garages and polynomial terms such as the number of square bathrooms, were considered important predictors. This research also involves a detailed residual analysis to ensure the accuracy of the model. The residual plot shows a random distribution of errors, indicating that the model fits the data well. This comprehensive analysis highlights not only the importance of various characteristics in determining home prices but also the value of advanced modeling techniques in improving forecast accuracy [4]. Previous studies of real estate prices have found significant effects of real estate characteristics on pricing. For example, Smith emphasized the importance of home size and the number of rooms in determining the price of a home [5]. Johnson and Brown emphasized the role of bathrooms and garages in their study of the suburban housing market [6]. Additionally, Thompson demonstrated the ===== PAGE 4 (score 17, numeric-paren 7, table-words 1, var-words 8) ===== 306 Figure 2. Distribution of House Prices 3.2. Correlation Analysis Based on correlation analysis of the variables that affect housing prices in suburban Chicago, several key observations can be made. The price of a house has a strong positive correlation with both the size (0.92) and the number of rooms (0.89), indicating that the larger and more spacious the house, the higher the price. In addition, the number of bedrooms (0.57) and the number of bathrooms (0.64) were also moderately associated with house prices, suggesting that homes with more bedrooms and bathrooms generally cost more. In addition, the annual tax (0.69) has a moderately positive correlation with house prices, meaning that the higher the house price, the higher the annual tax. The presence of a garage, while still positively correlated (0.39), was less correlated compared to other factors, suggesting that homes with more garage space are generally more expensive, but not as significant as other variables. Finally, the condition of the house has a weak correlation with the price of the house (0.28), suggesting that while the condition does affect the price of the house, it is not as strong as other factors such as space and number of rooms. These insights are critical to understanding the key factors driving home prices and can inform the decisions of buyers, sellers and real estate professionals (Figure 3). Figure 3. Correlation matrix ===== PAGE 5 (score 37, numeric-paren 0, table-words 10, var-words 12) ===== 307 3.3. Model Results In a detailed regression analysis of house prices in the suburbs of Chicago, several key factors were identified as significant predictors of property value. The study utilized a comprehensive dataset containing variables such as the number of bedrooms, square footage, number of rooms, lot size, annual taxes, number of bathrooms, number of garages, and the condition of the house. After filling missing values with median values to maintain data integrity, a linear regression model was constructed. The model's R-squared value was 0.732, indicating that approximately 73.2% of the variance in house prices could be explained by the model. Furthermore, the adjusted R-squared value of 0.714 confirmed the model's reliability, while the F-statistic of 39.70, with a p-value less than 0.001, underscored its statistical significance (Table 2). Table 2. Linear model results Variable Coefficient Standard Error T-value P-value 95% Confidence Interval Intercept 16.2463 3.633 4.472 0.000 9.051 to 23.442 Bedroom -3.3580 0.868 -3.869 0.000 -5.077 to -1.639 Space 0.0056 0.003 1.804 0.074 -0.001 to 0.012 Room 3.0901 0.950 3.253 0.001 1.209 to 4.971 Lot 0.2094 0.089 2.350 0.020 0.033 to 0.386 Tax 0.0062 0.002 2.636 0.010 0.002 to 0.011 Bathroom 5.6524 1.760 3.212 0.002 2.167 to 9.138 Garage 4.5383 0.911 4.984 0.000 2.735 to 6.342 Condition 0.5562 1.677 0.332 0.741 -2.766 to 3.878 The analysis revealed that larger houses with more rooms and higher square footage tend to command higher prices. Specifically, the number of rooms and the overall space were the strongest predictors, demonstrating the highest positive coefficients. Additionally, annual property taxes showed a significant positive correlation with house prices, reflecting the valuation and desirability of properties within specific neighborhoods. Interestingly, while the condition of the house was considered, it showed a weaker correlation compared to other factors, suggesting that buyers might prioritize size and space over condition. Features such as the presence of a garage and the number of bathrooms also positively influenced house prices, highlighting their importance to potential buyers. The coefficient for each variable shows the expected change in house prices with a one unit change in that variable, holding all other variables constant. For example, each additional room increases the price by approximately $3,090. Similarly, the presence of the garage adds about $4,538 to the price of the house. These insights provide valuable guidance for real estate professionals, buyers, and sellers in making informed decisions regarding property investments and pricing strategies. By understanding the key factors that drive house prices, stakeholders can better navigate the suburban housing market in Chicago, ensuring more accurate assessments and strategic planning. 4. Conclusion This study provides a comprehensive analysis of the factors that influence housing prices in suburban Chicago. Through detailed regression analysis, the author identified key variables that significantly affect the attribute values. Larger homes with more rooms, more square footage, and extras such as garages and bathrooms cost more. While the condition of a home does affect prices, its effect is weak compared to other factors. These findings provide valuable insights for real estate professionals, buyers and sellers, enabling them to make informed decisions regarding real estate investment and ===== PAGE 6 (score 3, numeric-paren 0, table-words 0, var-words 3) ===== 308 pricing strategies. 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