Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model
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 p...
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| Format: | Article |
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Vilnius Gediminas Technical University
2025-04-01
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| Series: | International Journal of Strategic Property Management |
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| Online Access: | https://ijspm.vgtu.lt/index.php/IJSPM/article/view/23639 |
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| author | Yilin Chen Haitao Zheng |
| author_facet | Yilin Chen Haitao Zheng |
| author_sort | Yilin Chen |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-955f778a5a5441e7bc540578e5cd90ed |
| institution | OA Journals |
| issn | 1648-715X 1648-9179 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | International Journal of Strategic Property Management |
| spelling | doaj-art-955f778a5a5441e7bc540578e5cd90ed2025-08-20T02:24:59ZengVilnius Gediminas Technical UniversityInternational Journal of Strategic Property Management1648-715X1648-91792025-04-0129210.3846/ijspm.2025.23639Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects modelYilin Chen0Haitao Zheng1Department of Statistics, School of Mathematics, Southwest Jiaotong University, Chengdu, ChinaDepartment of Statistics, School of Mathematics, Southwest Jiaotong University, Chengdu, China 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. https://ijspm.vgtu.lt/index.php/IJSPM/article/view/23639house price predictionspatiotemporal dataspatial dependencefunctional principal component analysisconditional autoregressive modelZHVI |
| spellingShingle | Yilin Chen Haitao Zheng Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model International Journal of Strategic Property Management house price prediction spatiotemporal data spatial dependence functional principal component analysis conditional autoregressive model ZHVI |
| title | Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model |
| title_full | Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model |
| title_fullStr | Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model |
| title_full_unstemmed | Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model |
| title_short | Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model |
| title_sort | spatiotemporal patterns and prediction of multi region house prices via functional mixed effects model |
| topic | house price prediction spatiotemporal data spatial dependence functional principal component analysis conditional autoregressive model ZHVI |
| url | https://ijspm.vgtu.lt/index.php/IJSPM/article/view/23639 |
| work_keys_str_mv | AT yilinchen spatiotemporalpatternsandpredictionofmultiregionhousepricesviafunctionalmixedeffectsmodel AT haitaozheng spatiotemporalpatternsandpredictionofmultiregionhousepricesviafunctionalmixedeffectsmodel |