AN INTERPRETABLE ANALYSIS OF RESIDENTIAL APARTMENT PRICES IN TASHKENT USING LINEAR REGRESSION
DOI:
https://doi.org/10.65164/zrcja795Ключевые слова:
Residential property prices; Linear regression; Housing market analysis; Spatial factors; Real estate listings; Data-driven analysis; Tashkent housing marketАннотация
Accurate understanding of residential housing price formation is essential for urban planning, real estate market transparency, and informed decision-making by buyers and sellers. In recent years, online real estate platforms have emerged as valuable sources of large-scale market data; however, empirical studies based on local housing data in Uzbekistan remain limited. This paper presents an interpretable analysis of residential apartment prices in Tashkent using multiple linear regression applied to real listing data collected from an online real estate platform.
The dataset consists of 7,421 apartment listings and includes structural characteristics (apartment size, number of rooms, floor level, and total building floors) as well as geographic attributes represented by latitude and longitude. A systematic preprocessing pipeline involving outlier removal and train–test splitting is employed. Several linear models, including ordinary least squares, Ridge, Lasso, and Elastic Net regression, are evaluated and compared using standard performance metrics.
Experimental results show that all linear variants achieve comparable performance, with an R² value of approximately 0.67 on the test set, indicating that a substantial proportion of price variability can be explained using linear relationships. The analysis highlights the dominant role of spatial location and apartment size in price formation, while regression coefficients provide clear interpretability of individual feature effects. Residual analysis confirms the absence of systematic prediction bias, with increased variability observed for higher-priced apartments.
Overall, the findings demonstrate that simple and interpretable linear regression models provide a robust baseline for residential price analysis in the Tashkent housing market. The study offers practical insights for market participants and lays the groundwork for future extensions incorporating nonlinear and spatially explicit modeling approaches
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