Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model
Abstract
House prices have always been a popular indicator for real estate market monitoring. This study explores the spatiotemporal patterns of house prices at the community level in San Francisco from January 2009 to April 2024. A functional spatiotemporal semiparametric mixed effects (FST-SM) model was proposed to analyze the Zillow Home Value Index (ZHVI), considering spatiotemporal variations. This response is associated with known influences and unknown latent random effects. The random-effects component was expanded using functional principal components. The conditional autoregressive (CAR) structure of the principal component scores was adopted to analyze nonparametric time trends and spatiotemporal correlations. The proposed model was compared with other time-series models in terms of spatiotemporal prediction. The results show that the prediction accuracy of the proposed model is higher than that of other regular models. In summary, a functional mixed effects model was proposed to describe spatiotemporal patterns and forecast house prices. This study can provide valuable references for decision-making by local governments, real estate suppliers, and house buyers.
Keyword : house price prediction, spatiotemporal data, spatial dependence, functional principal component analysis, conditional autoregressive model, ZHVI
How to Cite
Chen, Y., & Zheng, H. (2025). Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model. International Journal of Strategic Property Management, 29(2), 102–113. https://doi.org/10.3846/ijspm.2025.23639
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Syriopoulos, T., Makram, B., & Boubaker, A. (2015). Stock market volatility spillovers and portfolio hedging: BRICS and the financial crisis. International Review of Financial Analysis, 39, 7–18. https://doi.org/10.1016/j.irfa.2015.01.015
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Bhakta, K., Duggal, S., Fleisher, C., Karnati, P., & Tusnial, A. (2021). Forecasting local housing market trends using recurrent neural networks. https://www.pratyushakarnati.me/assets/documents/DL_Final_Report.pdf?i=1
Can, A. (1992). Specification and estimation of hedonic housing price models. Regional Science and Urban Economics, 22(3), 453–474. https://doi.org/10.1016/0166-0462(92)90039-4
Chen, X., Wei, L., & Xu, J. (2017). House price prediction using LSTM. arXiv. https://doi.org/10.48550/arXiv.1709.08432
Chiu, K. C. (2024). A long short-term memory model for forecasting housing prices in Taiwan in the post-epidemic era through big data analytics. Asia Pacific Management Review, 29(3), 273–283. https://doi.org/10.1016/j.apmrv.2023.08.002
Collazos, J. A. A., Dias, R., & Medeiros, M. C. (2023). Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID-19 in Brazil. Statistics in Medicine, 42(7), 993–1012. https://doi.org/10.1002/sim.9654
Crawford, G. W., & Fratantoni, M. C. (2003). Assessing the forecasting performance of regime-switching, ARIMA and GARCH models of house prices. Real Estate Economics, 31(2), 223–243. https://doi.org/10.1111/1540-6229.00064
Farhi, C., & Young, J. (2010). Forecasting residential rents: The case of Auckland, New Zealand. Pacific Rim Property Research Journal, 16(2), 207–220. https://doi.org/10.1080/14445921.2010.11104302
Gamber, W., Graham, J., & Yadav, A. (2023). Stuck at home: Housing demand during the COVID-19 pandemic. Journal of Housing Economics, 59, Article 101908. https://doi.org/10.1016/j.jhe.2022.101908
Ge, C. (2019). A LSTM and graph CNN combined network for community house price forecasting. In 2019 20th IEEE International Conference on Mobile Data Management (MDM) (pp. 393–394). https://doi.org/10.1109/MDM.2019.00-15
Gelfand, A. E. (2000). Gibbs sampling. Journal of the American Statistical Association, 95(452), 1300–1304. https://doi.org/10.2307/2669775
Glynn, C. (2022). Learning low-dimensional structure in house price indices. Applied Stochastic Models in Business and Industry, 38(1), 151–168. https://doi.org/10.1002/asmb.2653
Goetzmann, W. N., Peng, L., & Yen, J. (2012). The subprime crisis and house price appreciation. The Journal of Real Estate Finance and Economics, 44(1), 36–66. https://doi.org/10.1007/s11146-011-9321-4
Guo, J. Q., Chiang, S. H., Liu, M., Yang, C. C., & Gou, K. Y. (2020). Can machine learning algorithms associated with text mining from internet data improve housing price prediction performance? International Journal of Strategic Property Management, 24(5), 300–312. https://doi.org/10.3846/ijspm.2020.12742
Hael, M. A. (2023). Unveiling air pollution patterns in Yemen: A spatial-temporal functional data analysis. Environmental Science and Pollution Research, 30(17), 50067–50095. https://doi.org/10.1007/s11356-023-25790-3
Holt, J. R., & Borsuk, M. E. (2020). Using Zillow data to value green space amenities at the neighborhood scale. Urban Forestry & Urban Greening, 56, Article 126794. https://doi.org/10.1016/j.ufug.2020.126794
Howard, G., Liebersohn, J., & Ozimek, A. (2023). The short- and long-run effects of remote work on U.S. housing markets. Journal of Financial Economics, 150(1), 166–184. https://doi.org/10.1016/j.jfineco.2023.103705
Iacoviello, M. (2002). House prices and business cycles in Europe: A VAR analysis (Working Paper No. 540). Boston College, Department of Economics. https://EconPapers.repec.org/RePEc:boc:bocoec:540
Immergluck, D. (2011). Foreclosed: High-risk lending, deregulation, and the undermining of America’s mortgage market. Cornell University Press. https://doi.org/10.7591/9780801458828
Kim, H., Kwon, Y., & Choi, Y. (2020). Assessing the impact of public rental housing on the housing prices in proximity: Based on the regional and local level of price prediction models using long short-term memory (LSTM). Sustainability, 12(18), Article 7520. https://doi.org/10.3390/su12187520
Kim, J. (2024). Aging, housing prices, and young adults’ homeownership. Cities, 149, Article 104914. https://doi.org/10.1016/j.cities.2024.104914
Kong, J., & Kepili, E. (2023). A survey analysis: The current real estate marketing situation in the China greater bay area in the context of the COVID-19 epidemic. Real Estate Management and Valuation, 31(3), 1–19. https://doi.org/10.2478/remav-2023-0017
Krause, A. L., & Bitter, C. (2012). Spatial econometrics, land values and sustainability: Trends in real estate valuation research. Cities, 29, S19–S25. https://doi.org/10.1016/j.cities.2012.06.006
Lee, C. (2022). Forecasting spatially correlated targets: Simultaneous prediction of housing market activity across multiple areas. International Journal of Strategic Property Management, 26(2), 119–126. https://doi.org/10.3846/ijspm.2022.16786
Lee, C., & Park, K. (2018). Analyzing the rent-to-price ratio for the housing market at the micro-spatial scale. International Journal of Strategic Property Management, 22(3), 223–233. https://doi.org/10.3846/ijspm.2018.1416
Li, Y. H., & Guan, Y. T. (2014). Functional principal component analysis of spatiotemporal point processes with applications in disease surveillance. Journal of the American Statistical Association, 109(507), 1205–1215. https://doi.org/10.1080/01621459.2014.885434
Li, Y. H., Nguyen, D. V., Kürüm, E., Rhee, C. M., Banerjee, S., & Sentürk, D. (2022). Multilevel varying coefficient spatiotemporal model. Stat, 11(1), Article e438. https://doi.org/10.1002/sta4.438
Liu, S. T., & Su, Y. C. (2021). The impact of the COVID-19 pandemic on the demand for density: Evidence from the US housing market. Economics Letters, 207, Article 110010. https://doi.org/10.1016/j.econlet.2021.110010
Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. https://doi.org/10.2307/2332142
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87
Nunna, K. C., Zhou, Z., & Shakya, S. R. (2023, December 4–6). Time series forecasting of U.S. housing price index using machine and deep learning techniques. In 2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1–6). IEEE. https://doi.org/10.1109/CSDE59766.2023.10487733
Pijnenburg, K. (2017). The spatial dimension of US house prices. Urban Studies, 54(2), 466–481. https://doi.org/10.1177/0042098015606595
Ramsay, J. O., & Silverman, B. W. (2002). Applied functional data analysis: Methods and case studies. Springer. https://doi.org/10.1007/b98886
Ren, Y., Fox, E. B., & Bruce, A. (2017). Clustering correlated, sparse data streams to estimate a localized housing price index. The Annals of Applied Statistics, 11(2), 808–839. https://doi.org/10.1214/17-AOAS1019
Samal, K. K. R., Babu, K. S., & Das, S. K. (2022). Multi-output spatio-temporal air pollution forecasting using neural network approach. Applied Soft Computing, 126, Article 109316. https://doi.org/10.1016/j.asoc.2022.109316
San Francisco Mayor’s Office of Neighborhood Services. (2023). San Francisco find tool: Geographic locations and boundaries. San Francisco, USA. https://download.geofabrik.de/north-america/us/california/norcal-latest-free.shp.zip
Scheipl, F., Staicu, A. M., & Greven, S. (2015). Functional additive mixed models. Journal of Computational and Graphical Statistics, 24(2), 477–501. https://doi.org/10.1080/10618600.2014.901914
Selim, S. (2008). Determinants of house prices in Turkey: A hedonic regression model. Doğuş Üniversitesi Dergisi, 9(1), 65–76. https://doi.org/10.31671/dogus.2019.223
Shen, X., Wolfe, D. A., & Zhou, S. (1998). Local asymptotics for regression splines and confidence regions. The Annals of Statistics, 26(5), 1760–1782. https://doi.org/10.1214/aos/1024691356
Shi, S. (2023). Comparison of real estate price prediction based on LSTM and LGBM. Highlights in Science, Engineering and Technology, 49, 294–301. https://doi.org/10.54097/hset.v49i.8521
Straszheim, M. (1974). Hedonic estimation of housing market prices: A further comment. The Review of Economics and Statistics, 56(3), 404–406. https://doi.org/10.2307/1923985
Syriopoulos, T., Makram, B., & Boubaker, A. (2015). Stock market volatility spillovers and portfolio hedging: BRICS and the financial crisis. International Review of Financial Analysis, 39, 7–18. https://doi.org/10.1016/j.irfa.2015.01.015
United States Census Bureau. (2022). Physical housing characteristics for housing units. In American community survey. https://data.census.gov/table?t=Types+of+Rooms&g=050XX00US06075&y=2022
Yazdani, M. (2021). Machine learning, deep learning, and hedonic methods for real estate price prediction. arXiv. https://EconPapers.repec.org/RePEc:arx:papers:2110.07151
Zhang, H. Z., & Li, Y. H. (2022). Unified principal component analysis for sparse and dense functional data under spatial dependency. Journal of Business & Economic Statistics, 40(4), 1523–1537. https://doi.org/10.1080/07350015.2021.1938085
Zhang, H., Li, Y., & Branco, P. (2024). Describe the house and I will tell you the price: House price prediction with textual description data. Natural Language Engineering, 30(4), 661–695. https://doi.org/10.1017/S1351324923000360
Zhang, L., Baladandayuthapani, V., Zhu, H., Baggerly, K. A., Majewski, T., Czerniak, B. A., & Morris, J. S. (2016). Functional CAR models for large spatially correlated functional datasets. Journal of the American Statistical Association, 111(514), 772–786. https://doi.org/10.1080/01621459.2015.1042581
Zhao, Y. (2020). US housing market during COVID-19: Aggregate and distributional evidence (Working Paper No. 212). International Monetary Fund. https://doi.org/10.5089/9781513557816.001