Share:


Forecasting spatially correlated targets: simultaneous prediction of housing market activity across multiple areas

    Changro Lee Affiliation

Abstract

This study involved the development of an approach to forecast house prices and trading volumes across multiple areas simultaneously. Spatially correlated targets, such as house prices, can be predicted more accurately by leveraging the correlations across adjacent areas. A multi-output recurrent neural network, a deep learning algorithm specifically developed to analyze sequence data, was utilized to forecast the house prices and trading volumes in the four chosen study areas. The forecasting accuracy of future house prices in one of the four geographical areas clearly improved; this area was found to be a price-lagging area, and the forecasting accuracy of this area significantly increased by exploiting the information of a price-leading area. As for the prediction of trading volumes, the difference in performance between the multi-output recurrent neural network and conventional models was very small. The results of this study are expected to promote the use of deep learning to predict the housing market activity through a simultaneous forecasting framework.

Keyword : multi-output neural network, simultaneous prediction, correlation, house price, trading volume

How to Cite
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
Published in Issue
Apr 11, 2022
Abstract Views
84
PDF Downloads
105
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Ahmar, A. S., & Aidid, M. K. (2018). Crime modeling using spatial regression approach. Journal of Physics: Conference Series, 954(1), 012013. https://doi.org/10.1088/1742-6596/954/1/012013

Bakker, B. J., & Heskes, T. M. (2003). Task clustering and gating for Bayesian multitask learning. Journal of Machine Learning Research, 4, 83–99.

Ben-David, S., & Schuller, R. (2003). Exploiting task relatedness for multiple task learning. In Learning theory and kernel machines (pp. 567–580). Springer. https://doi.org/10.1007/978-3-540-45167-9_41

Beracha, E., Lang, M., & Hausler, J. (2019). On the relationship between market sentiment and commercial real estate performance – a textual analysis examination. Journal of Real Estate Research, 41(4), 605–638. https://doi.org/10.22300/0896-5803.41.4.605

Cellmer, R. (2013). Use of spatial autocorrelation to build regression models of transaction prices. Real Estate Management and Valuation, 21(4), 65–74. https://doi.org/10.2478/remav-2013-0038

Conway, D., Li, C. Q., Wolch, J., Kahle, C., & Jerrett, M. (2010). A spatial autocorrelation approach for examining the effects of urban greenspace on residential property values. The Journal of Real Estate Finance and Economics, 41(2), 150–169. https://doi.org/10.1007/s11146-008-9159-6

Cui, L., Xie, X., Shen, Z., Lu, R., & Wang, H. (2018). Prediction of the healthcare resource utilization using multi-output regression models. IISE Transactions on Healthcare Systems Engineering, 8(4), 291–302. https://doi.org/10.1080/24725579.2018.1512537

Dai, Z., Guldmann, J. M., & Hu, Y. (2018). Spatial regression models of park and land-use impacts on the urban heat island in central Beijing. Science of the Total Environment, 626, 1136–1147. https://doi.org/10.1016/j.scitotenv.2018.01.165

DeFusco, A., Ding, W., Ferreira, F., & Gyourko, J. (2018). The role of price spillovers in the American housing boom. Journal of Urban Economics, 108, 72–84. https://doi.org/10.1016/j.jue.2018.10.001

Fang, C., Liu, H., Li, G., Sun, D., & Miao, Z. (2015). Estimating the impact of urbanization on air quality in China using spatial regression models. Sustainability, 7(11), 15570–15592. https://doi.org/10.3390/su71115570

Futoma, J., Hariharan, S., Heller, K., Sendak, M., Brajer, N., Clement, M., & O’Brien, C. (2017, November). An improved multi-output Gaussian process rnn with real-time validation for early sepsis detection. In Machine Learning for Healthcare Conference (pp. 243–254). PMLR.

Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media.

Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 37(3), 424–438. https://doi.org/10.2307/1912791

Griffith, D. A., & Wong, D. W. (2007). Modeling population density across major US cities: a polycentric spatial regression approach. Journal of Geographical Systems, 9(1), 53–75. https://doi.org/10.1007/s10109-006-0032-y

He, L., Madathil, S. C., Servis, G., & Khasawneh, M. T. (2021). Neural network-based multi-task learning for inpatient flow classification and length of stay prediction. Applied Soft Computing, 108, 107483. https://doi.org/10.1016/j.asoc.2021.107483

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Ismail, S. (2006). Spatial autocorrelation and real estate studies: a literature review. Malaysian Journal of Real Estate, 1(1), 1–13.

Kang, Y., Zhang, F., Peng, W., Gao, S., Rao, J., Duarte, F., & Ratti, C. (2021). Understanding house price appreciation using multi-source big geo-data and machine learning. Land Use Policy, 111, 104919. https://doi.org/10.1016/j.landusepol.2020.104919

Lakshmanan, V., Robinson, S., & Munn, M. (2020). Machine learning design patterns. O’Reilly Media.

Law, S., Paige, B., & Russell, C. (2019). Take a look around: using street view and satellite images to estimate house prices. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), 1–19. https://doi.org/10.1145/3342240

Lee, C. (2021). Deep learning-based detection of tax frauds: an application to property acquisition tax. Data Technologies and Applications. https://doi.org/10.1108/DTA-06-2021-0134

Lee, K., Kim, H., & Shin, D. H. (2019). Forecasting short-term housing transaction volumes using time-series and internet search queries. KSCE Journal of Civil Engineering, 23(6), 2409–2416. https://doi.org/10.1007/s12205-019-1926-9

Li, Y., Li, K., & Tong, S. (2019). Adaptive neural network finite-time control for multi-input and multi-output nonlinear systems with positive powers of odd rational numbers. IEEE Transactions on Neural Networks and Learning Systems, 31(7), 2532–2543. https://doi.org/10.1109/TNNLS.2019.2933409

Moscone, F., & Tosetti, E. (2014). Spatial econometrics: theory and applications in health economics. Encyclopedia of Health Economics, 3, 329–334. https://doi.org/10.1016/B978-0-12-375678-7.00719-7

Perez, H., Tah, J. H., & Mosavi, A. (2019). Deep learning for detecting building defects using convolutional neural networks. Sensors, 19(16), 3556. https://doi.org/10.3390/s19163556

Poursaeed, O., Matera, T., & Belongie, S. (2018). Vision-based real estate price estimation. Machine Vision and Applications, 29(4), 667–676. https://doi.org/10.1007/s00138-018-0922-2

Rambaldi, A. N., & Rao, D. P. (2011). Hedonic predicted house price indices using time-varying hedonic models with spatial autocorrelation. School of Economics, University of Queensland.

Shi, H., Ma, Z., Chong, D., & He, W. (2021). The impact of Facebook on real estate sales. Journal of Management Analytics, 8(1), 101–112. https://doi.org/10.1080/23270012.2020.1858985

Taltavull de La Paz, P., López, E., & Juárez, F. (2017). Ripple effect on housing prices. Evidence from tourist markets in Alicante, Spain. International Journal of Strategic Property Management, 21(1), 1–14. https://doi.org/10.3846/1648715X.2016.1241192

Temur, A. S., Akgün, M., & Temur, G. (2019). Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models. Journal of Business Economics and Management, 20(5), 920–938. https://doi.org/10.3846/jbem.2019.10190

Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2), 234–240. https://doi.org/10.2307/143141

Tong, T., Yu, T. H. E., Cho, S. H., Jensen, K., & Ugarte, D. D. L. T. (2013). Evaluating the spatial spillover effects of transportation infrastructure on agricultural output across the United States. Journal of Transport Geography, 30, 47–55. https://doi.org/10.1016/j.jtrangeo.2013.03.001

Wang, W. C., Chang, Y. J., & Wang, H. C. (2019). An application of the spatial autocorrelation method on the change of real estate prices in Taitung City. ISPRS International Journal of Geo-Information, 8(6), 249. https://doi.org/10.3390/ijgi8060249

Xie, R., Fang, J., & Liu, C. (2016). Impact and spatial spillover effect of transport infrastructure on urban environment. Energy Procedia, 104, 227–232. https://doi.org/10.1016/j.egypro.2016.12.039

Xu, D., Shi, Y., Tsang, I. W., Ong, Y. S., Gong, C., & Shen, X. (2019). Survey on multi-output learning. IEEE Transactions on Neural Networks and Learning Systems, 31(7), 2409–2429. https://doi.org/10.1109/TNNLS.2019.2945133

Yang, J., Yu, Z., & Deng, Y. (2018). Housing price spillovers in China: a high-dimensional generalized VAR approach. Regional Science and Urban Economics, 68, 98–114. https://doi.org/10.1016/j.regsciurbeco.2017.10.016

Yilmaz, S., Haynes, K. E., & Dinc, M. (2002). Geographic and network neighbors: spillover effects of telecommunications infrastructure. Journal of Regional Science, 42(2), 339–360. https://doi.org/10.1111/1467-9787.00262

Zou, S., & Wang, L. (2021). Detecting individual abandoned houses from google street view: a hierarchical deep learning approach. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 298–310. https://doi.org/10.1016/j.isprsjprs.2021.03.020