1# RECORD c5910ffe5f4e46ed4d95f8ada01d97feb8173ca62# TITLE: Research on the Influencing Factors of House Prices in Chicago3# YEAR: 2024.0 VENUE: Transactions on Economics, Business and Management Research DOI: 10.62051/1jq1vs364# AUTHORS: Pin-Yao Liu5# PAGES: 6 CANDIDATE TABLE PAGES (1-based): [4, 5]6# 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.789===== PAGE 1 (score 17, numeric-paren 0, table-words 1, var-words 15) =====1011 12Transactions on Economics, Business and Management Research 13ISSN: 2960-1789, eISSN: 2960-2254 | Volume 10 14MEEA 2024 15 16Content from this work may be used under the terms of CC BY-NC 4.0 licence (https://creativecommons.org/licenses/by-nc/4.0/). 17Published by Warwick Evans Publishing. 18WEP19Warwick20Evans21Publishing22Research on the Influencing Factors of House Prices in Chicago 23Pinyao Liu* 24Business, University of Rochester, Rochester, 14627, The US 25* Corresponding Author Email: bulpy69@bu.edu 26Abstract. This study provides a comprehensive analysis of the factors that influence housing prices 27in suburban Chicago. Using detailed regression analysis, several key variables were identified as 28significant predictors of property value. The study included variables such as the number of 29bedrooms, square footage, number of rooms, size of the lot, annual taxes, number of bathrooms, 30number of garages, and the condition of the house. The R-squared value of the model is 0.732, 31indicating that approximately 73.2% of the house price variance can be explained by the selected 32variable. Houses with more rooms and more square footage tend to cost more. In addition, 33characteristics such as the number of garages and bathrooms can also have a significant impact on 34house prices. These findings provide valuable insights for real estate professionals, buyers and 35sellers, enabling them to make informed decisions regarding real estate investment and pricing 36strategies. By understanding the key drivers of house prices, stakeholders can better navigate the 37suburban housing market and ensure more accurate assessments and strategic planning. 38Keywords: House prices; Chicago suburbs; Real estate analysis; Regression model. 391. Introduction 40The analysis of home prices in suburban Chicago is an important area of study for understanding the 41dynamics of the real estate market. House prices are affected by a variety of factors, including but not 42limited to the size of the house, the number of bedrooms and bathrooms, lot size, annual taxes, the 43condition of the house, and the availability of amenities such as a garage. These variables interact in 44complex ways to determine the final price of the property. 45In this study, a dataset containing various characteristics associated with housing prices in suburban 46Chicago was used. The initial exploration, which included summary statistics and correlation analysis, 47revealed significant relationships between prices and several key variables. For example, the size of 48the house (Space) and the number of rooms (Room) show a strong positive correlation with price, 49indicating that the bigger the house, the more rooms, the more expensive the price [1]. In addition, 50the number of bathrooms and garages is also positively correlated with price, reflecting their 51importance in determining the value of a property [2]. To improve the predictive power of the model, 52polynomial features and interaction terms were combined. This approach allows for capturing 53nonlinear relationships and interactions between different variables. The polynomial regression 54model is significantly better than the linear model, explaining about 85.72% of the variance in house 55prices compared to 69.95% for the linear model [3]. Key interaction terms, such as the interaction 56between the number of bathrooms and garages and polynomial terms such as the number of square 57bathrooms, were considered important predictors. 58This research also involves a detailed residual analysis to ensure the accuracy of the model. The 59residual plot shows a random distribution of errors, indicating that the model fits the data well. This 60comprehensive analysis highlights not only the importance of various characteristics in determining 61home prices but also the value of advanced modeling techniques in improving forecast accuracy [4]. 62Previous studies of real estate prices have found significant effects of real estate characteristics on 63pricing. For example, Smith emphasized the importance of home size and the number of rooms in 64determining the price of a home [5]. Johnson and Brown emphasized the role of bathrooms and 65garages in their study of the suburban housing market [6]. Additionally, Thompson demonstrated the 666768===== PAGE 4 (score 17, numeric-paren 7, table-words 1, var-words 8) =====6970 71306 72 73Figure 2. Distribution of House Prices 743.2. Correlation Analysis 75Based on correlation analysis of the variables that affect housing prices in suburban Chicago, several 76key observations can be made. The price of a house has a strong positive correlation with both the 77size (0.92) and the number of rooms (0.89), indicating that the larger and more spacious the house, 78the higher the price. In addition, the number of bedrooms (0.57) and the number of bathrooms (0.64) 79were also moderately associated with house prices, suggesting that homes with more bedrooms and 80bathrooms generally cost more. In addition, the annual tax (0.69) has a moderately positive correlation 81with house prices, meaning that the higher the house price, the higher the annual tax. The presence 82of a garage, while still positively correlated (0.39), was less correlated compared to other factors, 83suggesting that homes with more garage space are generally more expensive, but not as significant 84as other variables. Finally, the condition of the house has a weak correlation with the price of the 85house (0.28), suggesting that while the condition does affect the price of the house, it is not as strong 86as other factors such as space and number of rooms. These insights are critical to understanding the 87key factors driving home prices and can inform the decisions of buyers, sellers and real estate 88professionals (Figure 3). 89 90Figure 3. Correlation matrix 919293===== PAGE 5 (score 37, numeric-paren 0, table-words 10, var-words 12) =====9495 96307 973.3. Model Results 98In a detailed regression analysis of house prices in the suburbs of Chicago, several key factors were 99identified as significant predictors of property value. The study utilized a comprehensive dataset 100containing variables such as the number of bedrooms, square footage, number of rooms, lot size, 101annual taxes, number of bathrooms, number of garages, and the condition of the house. After filling 102missing values with median values to maintain data integrity, a linear regression model was 103constructed. The model's R-squared value was 0.732, indicating that approximately 73.2% of the 104variance in house prices could be explained by the model. Furthermore, the adjusted R-squared value 105of 0.714 confirmed the model's reliability, while the F-statistic of 39.70, with a p-value less than 1060.001, underscored its statistical significance (Table 2). 107Table 2. Linear model results 108Variable 109Coefficient 110Standard Error 111T-value P-value 11295% Confidence Interval 113Intercept 11416.2463 1153.633 1164.472 1170.000 1189.051 to 23.442 119Bedroom 120-3.3580 1210.868 122-3.869 1230.000 124-5.077 to -1.639 125Space 1260.0056 1270.003 1281.804 1290.074 130-0.001 to 0.012 131Room 1323.0901 1330.950 1343.253 1350.001 1361.209 to 4.971 137Lot 1380.2094 1390.089 1402.350 1410.020 1420.033 to 0.386 143Tax 1440.0062 1450.002 1462.636 1470.010 1480.002 to 0.011 149Bathroom 1505.6524 1511.760 1523.212 1530.002 1542.167 to 9.138 155Garage 1564.5383 1570.911 1584.984 1590.000 1602.735 to 6.342 161Condition 1620.5562 1631.677 1640.332 1650.741 166-2.766 to 3.878 167 168The analysis revealed that larger houses with more rooms and higher square footage tend to command 169higher prices. Specifically, the number of rooms and the overall space were the strongest predictors, 170demonstrating the highest positive coefficients. Additionally, annual property taxes showed a 171significant positive correlation with house prices, reflecting the valuation and desirability of 172properties within specific neighborhoods. Interestingly, while the condition of the house was 173considered, it showed a weaker correlation compared to other factors, suggesting that buyers might 174prioritize size and space over condition. Features such as the presence of a garage and the number of 175bathrooms also positively influenced house prices, highlighting their importance to potential buyers. 176The coefficient for each variable shows the expected change in house prices with a one unit change 177in that variable, holding all other variables constant. For example, each additional room increases the 178price by approximately $3,090. Similarly, the presence of the garage adds about $4,538 to the price 179of the house. 180These insights provide valuable guidance for real estate professionals, buyers, and sellers in making 181informed decisions regarding property investments and pricing strategies. By understanding the key 182factors that drive house prices, stakeholders can better navigate the suburban housing market in 183Chicago, ensuring more accurate assessments and strategic planning. 1844. Conclusion 185This study provides a comprehensive analysis of the factors that influence housing prices in suburban 186Chicago. Through detailed regression analysis, the author identified key variables that significantly 187affect the attribute values. Larger homes with more rooms, more square footage, and extras such as 188garages and bathrooms cost more. While the condition of a home does affect prices, its effect is weak 189compared to other factors. These findings provide valuable insights for real estate professionals, 190buyers and sellers, enabling them to make informed decisions regarding real estate investment and 191192193===== PAGE 6 (score 3, numeric-paren 0, table-words 0, var-words 3) =====194195 196308 197pricing strategies. By understanding the key drivers of house prices, stakeholders can better navigate 198the suburban housing market and ensure more accurate assessments and strategic planning. 199References 200[1] J. Smith, Analyzing Real Estate Markets Using Machine Learning, Journal of Real Estate Research. 45(2) (2023) 201123-145. 202[2] L. Johnson, K. Brown, Factors Influencing House Prices: A Comparative Study, Housing Studies. 37(1) (2022) 89-203107. 204[3] H. Thompson, Advanced Regression Techniques in Real Estate Pricing, Journal of Statistical Analysis. 34(1) (2023) 20578-98. 206[4] B.T. 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