Evaluation and Analysis of Real Estate Investment Environment Based on Statistical Modeling: A Narrative Review
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Evaluation and Analysis of Real Estate Investment Environment Based on Statistical Modeling: A Narrative Review

Borui Li 1*
1 Shandong University of Finance and Economics
*Corresponding author: 1054971830@qq.com
Published on 30 July 2025
Volume Cover
AEMPS Vol.206
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-295-9
ISBN (Online): 978-1-80590-296-6
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Abstract

The evaluation of real estate investment environment constitutes a critical research domain for investment decision-making and regional economic development. This paper systematically reviews the progress in real estate investment environment assessment, with particular emphasis on the application of diverse statistical methodologies. Through comprehensive literature analysis, we identify that existing evaluation systems primarily construct indicator frameworks across five dimensions: macroeconomic conditions, policy regulations, market supply-demand dynamics, infrastructure, and social environment, employing quantitative techniques including factor analysis, regression modeling, and spatial econometrics. The comparative analysis examines the applicability, advantages, and limitations of various statistical models, with special focus on panel data models and machine learning applications in dynamic assessment. The findings demonstrate that the evolution from static analysis to dynamic prediction in real estate investment evaluation has been significantly enhanced through methodological innovations in statistics. The paper concludes by identifying current limitations in data quality and model interpretability, while proposing directions for future research.

Keywords:

Real estate investment, Investment environment evaluation, Statistical modeling, Factor analysis, Spatial econometrics

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Li,B. (2025). Evaluation and Analysis of Real Estate Investment Environment Based on Statistical Modeling: A Narrative Review. Advances in Economics, Management and Political Sciences,206,53-60.

References

[1]. Li, H., Wei, Y. D., & Yu, Z. (2016). Urban land expansion and spatial dynamics in globalizing Shanghai. Sustainability, 8(6), 537.

[2]. Tu, Y., & Bao, H. X. (2019). Housing price dynamics with development cycles: Evidence from China. Land Use Policy, 87, 104065.

[3]. Wu, J., Gyourko, J., & Deng, Y. (2012). Evaluating conditions in major Chinese housing markets. Regional Science and Urban Economics, 42(3), 531-543.

[4]. Zhang, L., Sun, T., & Feng, T. (2018). Housing price bubbles in China: A tale of 35 major cities. Applied Economics, 50(36), 3911-3923.

[5]. Chen, J., & Han, X. (2014). The evolution of the housing market and its socioeconomic impacts in the post-reform People's Republic of China: A survey of the literature. Journal of Economic Surveys, 28(4), 652-670.

[6]. Deng, Y., & Liu, P. (2019). Mortgage pre-payment and default behavior with embedded forward contract risks in China's housing market. Journal of Housing Economics, 43, 1-12.

[7]. Fang, H., Gu, Q., Xiong, W., & Zhou, L. A. (2015). Demystifying the Chinese housing boom. NBER Macroeconomics Annual, 30(1), 105-166.

[8]. Liu, Z., Wang, Y., & Tao, R. (2013). Urban land policies and housing prices: Evidence from Chinese cities. The Quarterly Review of Economics and Finance, 53(4), 352-359.

[9]. Ren, Y., Xiong, C., & Yuan, Y. (2012). House price bubbles in China. China Economic Review, 23(4), 786-800.

[10]. Elhorst, J. P. (2014). Spatial econometrics: From cross-sectional data to spatial panels. Springer.LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. CRC Press.

[11]. Anselin, L. (2005). Spatial statistical modeling in a GIS environment. In GIS for the urban environment (pp. 93-111). ESRI Press.

[12]. Wheaton, W. C. (1990). Vacancy, search, and prices in a housing market matching model. Journal of Political Economy, 98(6), 1270-1292.

[13]. Zhang, Y., & Sun, L. (2020). The impact of housing purchase restriction policies on urban housing prices: Evidence from 70 Chinese cities. Habitat International, 97, 102120.

[14]. Anselin, L., & Bera, A. K. (2003). Spatial dependence in linear regression models with an introduction to spatial econometrics. In Handbook of applied economic statistics (pp. 237-289). Marcel Dekker.

[15]. Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity, 2003(2), 299-362.

[16]. Case, K. E., & Shiller, R. J. (1989). The efficiency of the market for single-family homes. American Economic Review, 79(1), 125-137.

[17]. Chen, J., Guo, F., & Zhu, A. (2020). The heterogeneous impact of housing purchase restrictions on urban prices: Evidence from 283 Chinese cities. Journal of Housing Economics, 50, 101716.

[18]. Li, X., Wei, Y. D., & Yu, C. (2021). Spatial inequality of housing prices in China: Evidence from machine learning and spatial econometrics. Annals of the American Association of Geographers, 111(3), 835-856.

[19]. Gyourko, J., Mayer, C., & Sinai, T. (2022). Superstar cities and machine learning: Predicting commercial real estate price dynamics. Journal of Urban Economics, 130, 103456.

[20]. Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.

[21]. Hastie, T., Tibshirani, R., & Friedman, J. (2017). The elements of statistical learning (2nd ed.). Springer.

[22]. undberg, S. M., & Lee, S. I. (2020). SHAP values for explaining machine learning models. Advances in Neural Information Processing Systems, 33, 1-12.

[23]. Gu, Q., Zhou, L., & Yao, Y. (2021). Machine learning vs hedonic models: Asian commercial property valuation revisited. Real Estate Economics, 49(4), 1125-1158.

[24]. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, *30*, 4765–4774.

[25]. Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, *13*(1), 281-305.

[26]. Kok, S., Koponen, J., & Lönnqvist, H. (2022). Combining machine learning and econometrics for real estate price prediction. Journal of Housing Economics, *55*, 101817.

[27]. Wooldridge, J. M. (2020). Introductory econometrics: A modern approach (7th ed.). Cengage Learning.

[28]. Zhang, L., Wei, Y., & Zhang, P. (2023). Deep learning for real estate valuation: Performance and interpretability trade-offs. Journal of Property Research, 40(2), 145-167.

[29]. LeSage, J. P., & Pace, R. K. (2021). Spatial econometric model comparison using information criteria. Spatial Economic Analysis, 16(3), 312-331.

[30]. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1072.

[31]. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551-1580.

[32]. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817-838.

[33]. Zhang, Y., & Chen, J. (2021). Data quality in emerging real estate markets: Measurement errors and estimation biases. Journal of Real Estate Research, 43(3), 321-345.

[34]. Li, X., Wang, R., & Liu, Y. (2022). Machine learning in real estate prediction: A meta-analysis of model performance. Real Estate Economics, 50(4), 1128-1165

[35]. Chen, L. (2020). Measuring policy impacts in real estate: Beyond dummy variables. Urban Studies, 57(14), 2987-3010.

[36]. Wang, H., & Liu, Y. (2023). Mixed-frequency data modeling in real estate markets: A MIDAS approach. Journal of Real Estate Finance and Economics, 66(2), 189-215.

[37]. Thompson, E., & Garcia, R. (2023). A DSGE framework for real estate market analysis. Journal of Housing Economics, 59, 101936.

References

[1]. Li, H., Wei, Y. D., & Yu, Z. (2016). Urban land expansion and spatial dynamics in globalizing Shanghai. Sustainability, 8(6), 537.

[2]. Tu, Y., & Bao, H. X. (2019). Housing price dynamics with development cycles: Evidence from China. Land Use Policy, 87, 104065.

[3]. Wu, J., Gyourko, J., & Deng, Y. (2012). Evaluating conditions in major Chinese housing markets. Regional Science and Urban Economics, 42(3), 531-543.

[4]. Zhang, L., Sun, T., & Feng, T. (2018). Housing price bubbles in China: A tale of 35 major cities. Applied Economics, 50(36), 3911-3923.

[5]. Chen, J., & Han, X. (2014). The evolution of the housing market and its socioeconomic impacts in the post-reform People's Republic of China: A survey of the literature. Journal of Economic Surveys, 28(4), 652-670.

[6]. Deng, Y., & Liu, P. (2019). Mortgage pre-payment and default behavior with embedded forward contract risks in China's housing market. Journal of Housing Economics, 43, 1-12.

[7]. Fang, H., Gu, Q., Xiong, W., & Zhou, L. A. (2015). Demystifying the Chinese housing boom. NBER Macroeconomics Annual, 30(1), 105-166.

[8]. Liu, Z., Wang, Y., & Tao, R. (2013). Urban land policies and housing prices: Evidence from Chinese cities. The Quarterly Review of Economics and Finance, 53(4), 352-359.

[9]. Ren, Y., Xiong, C., & Yuan, Y. (2012). House price bubbles in China. China Economic Review, 23(4), 786-800.

[10]. Elhorst, J. P. (2014). Spatial econometrics: From cross-sectional data to spatial panels. Springer.LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. CRC Press.

[11]. Anselin, L. (2005). Spatial statistical modeling in a GIS environment. In GIS for the urban environment (pp. 93-111). ESRI Press.

[12]. Wheaton, W. C. (1990). Vacancy, search, and prices in a housing market matching model. Journal of Political Economy, 98(6), 1270-1292.

[13]. Zhang, Y., & Sun, L. (2020). The impact of housing purchase restriction policies on urban housing prices: Evidence from 70 Chinese cities. Habitat International, 97, 102120.

[14]. Anselin, L., & Bera, A. K. (2003). Spatial dependence in linear regression models with an introduction to spatial econometrics. In Handbook of applied economic statistics (pp. 237-289). Marcel Dekker.

[15]. Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity, 2003(2), 299-362.

[16]. Case, K. E., & Shiller, R. J. (1989). The efficiency of the market for single-family homes. American Economic Review, 79(1), 125-137.

[17]. Chen, J., Guo, F., & Zhu, A. (2020). The heterogeneous impact of housing purchase restrictions on urban prices: Evidence from 283 Chinese cities. Journal of Housing Economics, 50, 101716.

[18]. Li, X., Wei, Y. D., & Yu, C. (2021). Spatial inequality of housing prices in China: Evidence from machine learning and spatial econometrics. Annals of the American Association of Geographers, 111(3), 835-856.

[19]. Gyourko, J., Mayer, C., & Sinai, T. (2022). Superstar cities and machine learning: Predicting commercial real estate price dynamics. Journal of Urban Economics, 130, 103456.

[20]. Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.

[21]. Hastie, T., Tibshirani, R., & Friedman, J. (2017). The elements of statistical learning (2nd ed.). Springer.

[22]. undberg, S. M., & Lee, S. I. (2020). SHAP values for explaining machine learning models. Advances in Neural Information Processing Systems, 33, 1-12.

[23]. Gu, Q., Zhou, L., & Yao, Y. (2021). Machine learning vs hedonic models: Asian commercial property valuation revisited. Real Estate Economics, 49(4), 1125-1158.

[24]. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, *30*, 4765–4774.

[25]. Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, *13*(1), 281-305.

[26]. Kok, S., Koponen, J., & Lönnqvist, H. (2022). Combining machine learning and econometrics for real estate price prediction. Journal of Housing Economics, *55*, 101817.

[27]. Wooldridge, J. M. (2020). Introductory econometrics: A modern approach (7th ed.). Cengage Learning.

[28]. Zhang, L., Wei, Y., & Zhang, P. (2023). Deep learning for real estate valuation: Performance and interpretability trade-offs. Journal of Property Research, 40(2), 145-167.

[29]. LeSage, J. P., & Pace, R. K. (2021). Spatial econometric model comparison using information criteria. Spatial Economic Analysis, 16(3), 312-331.

[30]. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1072.

[31]. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551-1580.

[32]. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817-838.

[33]. Zhang, Y., & Chen, J. (2021). Data quality in emerging real estate markets: Measurement errors and estimation biases. Journal of Real Estate Research, 43(3), 321-345.

[34]. Li, X., Wang, R., & Liu, Y. (2022). Machine learning in real estate prediction: A meta-analysis of model performance. Real Estate Economics, 50(4), 1128-1165

[35]. Chen, L. (2020). Measuring policy impacts in real estate: Beyond dummy variables. Urban Studies, 57(14), 2987-3010.

[36]. Wang, H., & Liu, Y. (2023). Mixed-frequency data modeling in real estate markets: A MIDAS approach. Journal of Real Estate Finance and Economics, 66(2), 189-215.

[37]. Thompson, E., & Garcia, R. (2023). A DSGE framework for real estate market analysis. Journal of Housing Economics, 59, 101936.

Cite this article

Li,B. (2025). Evaluation and Analysis of Real Estate Investment Environment Based on Statistical Modeling: A Narrative Review. Advances in Economics, Management and Political Sciences,206,53-60.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Digital Transformation in Global Human Resource Management

ISBN: 978-1-80590-295-9(Print) / 978-1-80590-296-6(Online)
Editor: Florian Marcel Nuţă Nuţă, An Nguyen
Conference website: https://www.icemgd.org/committee.html
Conference date: 26 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.206
ISSN: 2754-1169(Print) / 2754-1177(Online)

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