Scientific Journal SPATIAL ECONOMICS - Evolution of Spatial Econometric Models: Application to Real Estate Market Assessment

In the section	Review, Discussion, Criticism
Title of the article	Evolution of Spatial Econometric Models: Application to Real Estate Market Assessment
Pages	160-184
Author	Lev Igorevich Kerman Postgraduate Student Novosibirsk State Technical University, Faculty of Business 20 Karl Marx Av., Novosibirsk, 630073, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0009-0004-5583-4624
Abstract	The article examines the evolution of spatial econometric models applied to real estate market analysis – from hedonic regression models to modern spatio-temporal volatility models. Based on a systematized literature review, the study describes the prerequisites for the transition from traditional valuation approaches (sales comparison, income, and cost) to multiple regression and further to global spatial autoregressive models (SAR, SEM and their extensions: SDM, SARMA, SAC).Local models based on the spatially varying coefficients (SVC) approach are discussed, including both discrete and continuous specification types (spatial regime models, deterministic and stochastic formulations). Special attention is given to conditional variance models: from temporal (G)ARCH models to their spatial and spatio-temporal extensions that account for the propagation of volatility across neighboring markets. A two-level model classification is proposed according to: (1) the type of moment of the distribution being modeled (mean or variance), and (2) the nature of spatial heterogeneity (global or local). In addition, three research scales of analysis are distinguished: spatial, temporal, and spatio-temporal. The study provides an integrated perspective on the development of analytical tools based on the literature, highlighting their practical relevance for mass real estate appraisal and price volatility analysis
Code	519.86+330.4
JEL	C21; C33; C58; R31; R32
DOI	https://dx.doi.org/10.14530/se.2026.1.160-184
Keywords	spatial econometrics, mass real estate appraisal, housing market, spatio-temporal models, volatility modeling, hedonic regression, spatial factors
Download	SE.2026.1.160-184.Kerman.pdf
For citation	Kerman L.I. Evolution of Spatial Econometric Models: Application to Real Estate Market Assessment. Prostranstvennaya Ekonomika = Spatial Economics, 2026, vol. 22, no. 1, pp. 160–184. https://dx.doi.org/10.14530/se.2026.1.160-184 (In Russian)
References	1. Anselin L. Local Indicators of Spatial Association – LISA // Geographical Analysis. 1995. Vol. 27. Issue 2. Pр. 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x 2. Anselin L. Spatial Econometrics: Methods and Models. Dordrecht: Springer Netherlands, 1988, 284 p. 3. Anselin L., Amaral P. Endogenous Spatial Regimes. Journal of Geographical Systems, 2024, vol. 26, no. 2, pр. 209–234. https://doi.org/10.1007/s10109-023-00411-2 4. Balash V.A., Balash O.S., Harlamov A.V. A Spatial Econometric Analysis of the Housing Market. Prikladnaya Ekonometrika = Applied Econometrics, 2011, no. 22 (2), pp. 62–77. (In Russian). 5. Baum A., Crosby N. Property Investment Appraisal. London: Routledge, 1989, 264 р. 6. Baum A., Mackmin D., Nunnington N. The Income Approach to Property Valuation. London: Routledge, 2017, 408 р. 7. Belyaeva A.V. Comparing the Effectiveness of Computer Mass Appraisal Methods. Kompyuternye Issledovaniya i Modelirovanie = Computer Research and Modeling, 2015, vol. 7, no. 1, pp(In Russian). 8. Belyaeva A.V., Grebenuk E.A. Real Estate Mass Appraisal Models Design with the Spatial Dependence. Problemy Upravleniya [Management Issues], 2014, no. 1, pp. 45–52. (In Russian). 9. Bobrovskaya E.D., Polbin A.V. Econometric Modeling of the Demand for Short-Term Rental Housing: The Case of Airbnb in Moscow. Zhurnal Novoy Ekonomicheskoy Assotsiatsii = Journal of the New Economic Association, 2023, no. 2 (59), pp. 64–84. https://doi.org/10.31737/22212264_2023_2_64-84 (In Russian). 10. Bollerslev T. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 1986, vol. 31, issue 3, pр. 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 11. Borovkova S., Lopuhaa R. Spatial GARCH: A Spatial Approach to Multivariate Volatility Modeling. SSRN Electronic Journal, 2012. https://doi.org/10.2139/ssrn.2176781 12. Brunsdon C., Fotheringham A.S., Charlton M.E. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 1996, vol. 28, issue 4, pp. 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x 13. Burnham K.P., Anderson D.R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. New York: Springer, 2004, 488 p. 14. Casetti E. Generating Models by the Expansion Method: Applications to Geographical Research. Geographical Analysis, 1972, vol. 4, issue 1, pp. 81–91. https://doi.org/10.1111/j.1538-4632.1972.tb00458.x 15. Casetti E. The Expansion Method, Mathematical Modeling, and Spatial Econometrics. International Regional Science Review, 1997, vol. 20, issue 1–2, pp. 9–33. https://doi.org/10.1177/016001769702000102 16. Chen S., Zhuang D., Zhang H. GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China. Complexity, 2020, pp. 1–16. https://doi.org/10.1155/2020/1079024 17. Cohen J.P., Coughlin C.C. Spatial Hedonic Models of Airport Noise, Proximity, and Housing Prices. Journal of Regional Science, 2008, vol. 48, issue 5, pp. 859–878. https://doi.org/10.1111/j.1467-9787.2008.00569.x 18. Cohen J.P., Coughlin C.C., Zabel J. Time-Geographically Weighted Regressions and Residential Property Value Assessment. Journal of Real Estate Finance and Economics, 2020, vol. 60, pp. 134–154. https://doi.org/10.1007/s11146-019-09718-8 19. D’Amato M. International Real Estate Review. International Real Estate Review, 2007, vol. 10, no. 2, pp. 42–65. https://doi.org/10.53383/100083 20. Dogan O., Tasp?nar S. Bayesian Inference in Spatial GARCH Models: An Application to US House Price Returns. Spatial Economic Analysis, 2023, vol. 18, issue 3, pp. 410–428. https://doi.org/10.1080/17421772.2022.2123553 21. Dubin R.A. Predicting House Prices Using Multiple Listings Data. The Journal of Real Estate Finance and Economics, 1998, vol. 17, pp. 35–59. https://doi.org/10.1023/A:1007751112669 22. Elhorst J.P. Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Berlin; Heidelberg: Springer, 2014, 120 p. 23. Engle R.F. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 1982, vol. 50, no. 4, pp. 987–1007. https://doi.org/10.2307/1912773 24. Fotheringham A.S., Charlton M., Brunsdon C. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: John Wiley & Sons Ltd., 2002, 282 p. 25. Fotheringham A.S., Yang W., Kang W. Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 2017, vol. 107, issue 6, pp. 1247–1265. https://doi.org/10.1080/24694452.2017.1352480 26. Fotheringham S.A., Park B. Localized Spatiotemporal Effects in the Determinants of Property Prices: A Case Study of Seoul. Applied Spatial Analysis and Policy, 2018, vol. 11, pp. 581–598. https://doi.org/10.1007/s12061-017-9232-8 27. Gelfand A.E., Kim H.-J., Sirmans C.F., Banerjee S. Spatial Modeling with Spatially Varying Coefficient Processes. Journal of the American Statistical Association, 2003, vol. 98, issue 462, pp. 387–396. https://doi.org/10.1198/016214503000170 28. Getis A. Cliff A.D., Ord J.K. Spatial Autocorrelation. Progress in Human Geography, 1995, vol. 19, issue 2, pp. 245–249. https://doi.org/10.1177/030913259501900205 29. Goodman A.C. Andrew Court and the Invention of Hedonic Price Analysis. Journal of Urban Economics, 1998, vol. 44, issue 2, pp. 291–298. https://doi.org/10.1006/juec.1997.2071 30. Griffith D.A. Spatially Autoregressive Models. International Encyclopedia of Human Geography. Amsterdam: Elsevier, 2009, pp. 396–402. 31. Harlamov A.V. Analysis of Dynamics of Prices of Real Estateby Method of Geographically Weighted Regression. Izvestiya Saratovskogo Universiteta. Novaya Seriya. Seriya: Ekonomika. Upravlenie. Pravo = Izvestiya of Saratov University. Economics. Management. Law, 2011, vol. 11, issue 2, pp. 25–29. (In Russian). 32. Holleland S., Karlsen H.A. A Stationary Spatio-Temporal GARCH Model. Journal of Time Series Analysis, 2020, vol. 41, issue 2, pp. 177–209. https://doi.org/10.1111/jtsa.12498 33. Huang B., Wu B., Barry M. Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices. International Journal of Geographical Information Science, 2010, vol. 24, issue 3, pp. 383–401. https://doi.org/10.1080/13658810802672469 34. Isaac D., O’Leary J. Property Valuation Techniques. London: Bloomsbury Visual Arts, 2013, 266 p. 35. Lan F., Zhang Y. Spatial Autoregressive Model of Commodity Housing Price and Empirical Research. Systems Engineering Procedia, 2011, vol. 1, pр. 206–212. https://doi.org/10.1016/j.sepro.2011.08.033 36. Laurini M.P. A Continuous Spatio-Temporal Model for House Prices in the USA. The Annals of Regional Science, 2017, vol. 58, pр. 235–269. https://doi.org/10.1007/s00168-016-0801-6 37. Lentz J. Developing a Geospatial Protocol for Coral Epizootiology. 2012, 300 p. https://doi.org/10.13140/RG.2.1.4709.8649 38. LeSage J., Pace R.K. Introduction to Spatial Econometrics. Boca Raton: Chapman and Hall/CRC, 2009, 376 p. 39. LeSage J.P., Pace R.K. Interpreting Spatial Econometric Models. Handbook of Regional Science. Edited by M. Fischer, P. Nijkamp. Berlin; Heidelberg: Springer, 2018, pp. 1–18. https://doi.org/10.1007/978-3-642-36203-3_91-1 40. Liang X., Liu Y., Qiu T., Jing Y., Fang F. The Effects of Locational Factors on the Housing Prices of Residential Communities: The Case of Ningbo, China. Habitat International, 2018, vol. 81, pp. 1–11. https://doi.org/10.1016/j.habitatint.2018.09.004 41. Lindgren F., Rue H., Lindstrom J. An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach // Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2011. Vol. 73. Issue 4. Pp. 423–498. https://doi.org/10.1111/j.1467-9868.2011.00777.x 42. Ling Y. Time, Space and Hedonic Prediction Accuracy: Evidence from Corsican Apartment Markets. The Annals of Regional Science, 2020, vol. 64, pp. 367–388. https://doi.org/10.1007/s00168-019-00967-2 43. Mass Appraisal Methods: An International Perspective for Property Valuers. Edited by T. Kauko, M.D’Amato. Chichester: Wiley-Blackwell, 2008, 360 p. 44. McCluskey W.J., Borst R. An Evaluation of MRA, Comparable Sales Analysis and ANNs for the Mass Appraisal of Residential Properties in Northern Ireland. Assessment Journal, 1997, vol. 4, pp. 47–55. 45. Miles W. Volatility Clustering in U.S. Home Prices. Journal of Real Estate Research, 2008, vol. 30, issue 1, pp. 73–90. https://doi.org/10.1080/10835547.2008.12091211 46. Millington A. An Introduction to Property Valuation. London: Estates Gazette, 2013, 288 p. 47. Muto S., Sugasawa S., Suzuki M. Hedonic Real Estate Price Estimation with the Spatiotemporal Geostatistical Model. Journal of Spatial Econometrics, 2023, vol. 4, 10. https://doi.org/10.1007/s43071-023-00039-w 48. Oshan T., Li Z., Kang W., Wolf L., Fotheringham A. MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. ISPRS International Journal of Geo-Information, 2019, vol. 8, issue 6, 269. https://doi.org/10.3390/ijgi8060269 49. Osland L. An Application of Spatial Econometrics in Relation to Hedonic House Price Modeling. Journal of Real Estate Research, 2010, vol. 32, issue 3, pp. 289–320. https://doi.org/10.1080/10835547.2010.12091282 50. Otto P. A Multivariate Spatial and Spatiotemporal ARCH Model. Spatial Statistics, 2024, vol. 60, 100823. https://doi.org/10.1016/j.spasta.2024.100823 51. Otto P., Dogan O., Tasp?nar S., Schmid W., Bera A.K. Spatial and Spatiotemporal Volatility Models: A Review. Journal of Economic Surveys, 2025, vol. 39, issue 3, pp. 1037–1091. https://doi.org/10.1111/joes.12643 52. Pace R.K., Barry R. Quick Computation of Spatial Autoregressive Estimators. Geographical Analysis, 1997, vol. 29, issue 3, pp. 232–247. https://doi.org/10.1111/j.1538-4632.1997.tb00959.x 53. Pace R.K., Barry R., Clapp J.M., Rodriquez M. Spatio-Temporal Autoregressive Models of Neighborhood Effects. Journal of Real Estate Finance and Economics, 1998, vol. 17, pp. 15–33. https://doi.org/10.1023/A:1007799028599 54. Pagourtzi E., Assimakopoulos V., Hatzichristos T., French N. Real Estate Appraisal: A Review of Valuation Methods. Journal of Property Investment & Finance, 2003, vol. 21, issue 4, pp. 383–401. https://doi.org/10.1108/14635780310483656 55. Price Indexes and Quality Change. Edited by Z. Griliches. Cambridge: Harvard University Press, 1971. 298 p. 56. Rasmussen C.E., Williams C.K.I. Gaussian Processes for Machine Learning. Cambridge: MIT Press, 2005. 248 p. 57. Rocio V.D., Laurini M.P. Bayesian Spatio-Temporal Modeling of Real Estate Launch Prices. Journal of Spatial Econometrics, 2023, vol. 4, 14. https://doi.org/10.1007/s43071-023-00044-z 58. Rosen S. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy, 1974, vol. 82, no. 1, pp. 34–55. https://doi.org/10.1086/260169 59. Rue H., Held L. Gaussian Markov Random Fields: Theory and Applications. Boca Raton: Chapman and Hall/CRC, 2005, 280 p. 60. Rue H., Martino S., Chopin N. Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2009, vol. 71, no. 2, pp. 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x 61. Salieva А.К. Econometric Modeling of the Influence of Factors on Housing Prices in Orenburg based on Geographically Weighted Regression. Mathematical Modeling in Economics, Insurance and Risk Management: Proceedings of the IV International Youth Scientific and Practical Conference in 2 Volumes. Vol. 1. Moscow, 2015, pp. 211–216. (In Russian). 62. Sato T., Matsuda Y. Spatial Extension of Generalized Autoregressive Conditional Heteroskedasticity Models. Spatial Economic Analysis, 2021, vol. 16, issue 2, pp. 148–160. https://doi.org/10.1080/17421772.2020.1742929 63. Scarrett D. Property Valuation: The Five Methods. London: Routledge, 2008, 196 p. 64. Scarrett D., Osborn S. Property Valuation. London: Routledge, 2014, 196 p. 65. Soltani A., Pettit C.J., Heydari M., Aghaei F. Housing Price Variations Using Spatio-Temporal Data Mining Techniques. Journal of Housing and the Built Environment, 2021, vol. 36, pp. 1199–1227. https://doi.org/10.1007/s10901-020-09811-y 66. Sternik G.M., Sternik S.G. Methodology of Housing Market Modeling and Forecasting. Moscow, 2018, 586 p. (In Russian). 67. Sternik G.M., Sternik S.G. Real Estate Market Analysis for Professionals. Edited by G.F. Ozhgikhina. Moscow, 2009, 606 p. (In Russian). 68. Tasp?nar S., Dogan O., Chae J., Bera A.K. Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago. Oxford Bulletin of Economics and Statistics, 2021, vol. 83, issue 5, pp. 1243–1272. https://doi.org/10.1111/obes.12425 69. Tindova M.G., Maximov A.A. Econometric Analysis of Pricing Residential Real Estate. Modeli, Sistemy, Seti v Ekonomike, Tekhnike, Prirode i Obshchestve = Models, Systems, Networks in Economics, Engineering, Nature and Society, 2016, no. 2 (18), pp. 71–78. (In Russian). 70. Wang D., Li V.J., Yu H. Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area. Land, 2020, vol. 9, issue 5, 143. https://doi.org/10.3390/land9050143 71. Wu Y., Wei Y. D., Li H. Analyzing Spatial Heterogeneity of Housing Prices Using Large Datasets. Applied Spatial Analysis and Policy, 2020, vol. 13, pp. 223–256. https://doi.org/10.1007/s12061-019-09301-x 72. Wyatt P. Property Valuation. Chichester: Wiley-Blackwell, 2023, 560 p. 73. Xu S., Zhang Z. Spatial Differentiation and Influencing Factors of Second-Hand Housing Prices: A Case Study of Binhu New District, Hefei City, Anhui Province, China. Journal of Mathematics, 2021, vol. 2, pр. 1–8. https://doi.org/10.1155/2021/8792550
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Submitted	20.10.2025
Approved after reviewing	09.02.2026
Accepted for publication	04.03.2026
Available online	10.04.2026