Model Hybrid for Sales Forecast for the Housing Market of São Paulo

ABECIP. Crédito imobiliário com recursos da poupança cresceu 3,4% em 2014. (2015). https://www.abecip.org.br/imprensa/abecip-na-midia/credito-imobiliario-com-recursos-dapoupanca-cresceu-3-4-em-2014. Acessed 15 jan. 2019.Search in Google Scholar

Anghel, I., & Hristea, M. (2015). Some considerations regarding the international real estate market – present and future predications. Procedia Economics and Finance, 32, 1442–1452. Advance online publication. https://doi.org/10.1016/S2212-5671(15)01520-810.1016/S2212-5671(15)01520-8Search in Google Scholar

Baghestani, H. (2017). Do consumers’ home buying attitudes explain the behaviour of US home sales? Applied Economics Letters, 24, 779–783. Advance online publication. https://doi.org/10.1080/13504851.2016.122940110.1080/13504851.2016.1229401Search in Google Scholar

Ball, M., & Nanda, A. (2013). Household attributes and the future demand for retirement housing. International Journal of Housing Markets and Analysis, 6(1). https://doi.org/10.1108/1753827131130600210.1108/17538271311306002Search in Google Scholar

Ballou, R. (2006). Gerenciamento da cadeia de suprimentos/Logística empresarial (5th ed.). Bookman.Search in Google Scholar

Baltrusis, N., & Mourad, L. N. (2014). Política habitacional e locação social em Salvador. Caderno CRH, 27(71), 267–284. https://doi.org/10.1590/S0103-4979201400020000410.1590/S0103-49792014000200004Search in Google Scholar

Box, G., Jenkins, G., & Reinsel, G. (2008). Time series analysis – forecasting and control (4th ed.). Prentice Hall.Search in Google Scholar

Bratu, M. (2012). The reduction of uncertainty in making decisions by evaluating the macroeconomic forecasts performance in Romania. Economic Research Journal, 25, 239–262. Advance online publication. https://doi.org/10.1080/1331677X.2012.1151750610.1080/1331677X.2012.11517506Search in Google Scholar

Chen, L. H. (2010). Using Financial and Macroeconomic Indicators to Forecast Sales of Large Development and Construction Firms. The Journal of Real Estate Finance and Economics, 40, 310–331. Advance online publication. https://doi.org/10.1007/s11146-008-9158-710.1007/s11146-008-9158-7Search in Google Scholar

Csesko, M., & Reed, R. (2009). Will residential aged care facilities meet long-term demand? Property Management, 27(1). https://doi.org/10.1108/0263747091093267410.1108/02637470910932674Search in Google Scholar

Dua, P., & Miller, S. M. (1996). Forecasting Connecticut home sales in a BVAR framework using coincident and leading indexes. The Journal of Real Estate Finance and Economics, 13. Advance online publication. https://doi.org/10.1007/BF0021739210.1007/BF00217392Search in Google Scholar

Dua, P., Miller, S. M., & Smyth, D. J. (1999). Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework. The Journal of Real Estate Finance and Economics, 18, 191–205. Advance online publication. https://doi.org/10.1023/A:100771872560910.1023/A:1007718725609Search in Google Scholar

Dickey, D., & Fuller, W. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association. Advance online publication. https://doi.org/10.2307/228634810.2307/2286348Search in Google Scholar

EMBRAESP. (2013). Crise estrutural no setor imobiliário ou aonde que se ergueu no mar. https://incorporacaoimobiliaria.com/tag/embraesp/. Acessed 26 dec 2016.Search in Google Scholar

Gibler, K. M., & Sherwood Clements, J., III. (2011). Testing a forecast model to predict movement of older Americans into retirement housing. International Journal of Housing Markets and Analysis, 4(1). https://doi.org/10.1108/1753827111111181110.1108/17538271111111811Search in Google Scholar

Gibler, K. M., Talvavull, P., Casado-Díaz, J. M., & Rodriguez, V. (2009). Examining retirement housing preferences among international retiree migrants. International Real Estate Review, 12(1), 1–22.10.53383/100102Search in Google Scholar

Granger, C. W., & Ramanathan, R. (1984). Improved methods of combining forecasts. Journal of Forecasting, 3, 197–204. Advance online publication. https://doi.org/10.1002/for.398003020710.1002/for.3980030207Search in Google Scholar

Gupta, R., Lau, C. K.M., Plakandaras, V., & Wong, W. (2019). The role of housing sentiment in forecasting U.S. home sales growth: evidence from a Bayesian compressed vector autoregressive model. Economic Research-Ekonomska Istraživanja, 32(1). https://doi.org/10.1080/1331677X.2019.165065710.1080/1331677X.2019.1650657Search in Google Scholar

Hassani, H., Ghodsi, Z., Gupta, R., & Segnon, M. (2017). Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis. Computational Economics, 49, 83–97. Advance online publication. https://doi.org/10.1007/s10614-015-9548-x10.1007/s10614-015-9548-xSearch in Google Scholar

Huang, Y., Wang, Y., & Gai, S. (2011). The application and research of a new combinatorial analysis and forecasting method in real estate area based on Grey System Theory and Multivariate Linear Regression. Procedia Engineering, 15, 4532–4537. Advance online publication. https://doi.org/10.1016/j.proeng.2011.08.85110.1016/j.proeng.2011.08.851Search in Google Scholar

Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18, 439–454. Advance online publication. https://doi.org/10.1016/S0169-2070(01)00110-810.1016/S0169-2070(01)00110-8Search in Google Scholar

Hyung, C. J., & Jung, J. U. (2013). Simulation Model on Long-Term Housing Demand Forecasting for the Utilization of Real Estate Knowledge. Journal of CEO and Management Studies, 16, 119–139.Search in Google Scholar

IBGE. (2014). Pesquisa anual dos serviços. http://ibge.gov.br/home/estatistica/economia/comercioeservico/pas/analisepas99.shtm. Acessed 02 dec. 2018.Search in Google Scholar

IBGE. (2016). Série histórica IPCA. http://www.ibge.gov.br/home/estatistica/indicadores/precos/inpc_ipca/ipcainpc_201601_1.shtm. Acessed 27 jan. 2019.Search in Google Scholar

ISTOÉ. Mercado imobiliário fecha 2018 com alta de 19,2% em vendas, diz CBIC. (2019). https://istoe.com.br/mercado-imobiliario-fecha-2018-com-alta-de-192-em-vendas-diz-cbic/. Acessed 02 jan. 2020. JAFARI, A., AKHAVIAN, R. Driving forces for the US residential housing price: a predictive analysis. Built Environment Project and Asset Management. Article In Press.Search in Google Scholar

Khashei, M., & Bijari, M. (2010). An artificial neural network (p,d,q) model for time series forecasting. Expert Systems with Applications, 37, 479–489. Advance online publication. https://doi.org/10.1016/j.eswa.2009.05.04410.1016/j.eswa.2009.05.044Search in Google Scholar

Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root? Journal of Econometrics. Advance online publication. https://doi.org/10.1016/0304-4076(92)90104-Y10.1016/0304-4076(92)90104-YSearch in Google Scholar

Liu, L., & Li, W. (2009). Research on the forecast of Real Estate market demand in Beijing. Paper presentation at International Conference on Construction & Real Estate Management, Beijing, China.Search in Google Scholar

Mendonça, M. J., Medrano, L. A., & Sachsida, A. (2011). Avaliando o efeito de um choque de política monetária sobre o mercado imobiliário. Texto para Discussão IPEA, 1631, 1–31.Search in Google Scholar

Mendonça, M. J., & Sachsida, A. (2012). Existe bolha no mercado imobiliário brasileiro? Textos para Discussão IPEA., 1762, 1–66.Search in Google Scholar

Mendonça, M. J. (2013). O crédito imobiliário no Brasil e sua relação com a Política Monetária. Revista brasileira de Economia 67(4). https://doi.org/10.1590/S0034-7140201300040000510.1590/S0034-71402013000400005Search in Google Scholar

Moro, M. F., Reis, C. C. C., Flores, S., Weise, A., & Vincenzi, S. (2016a). São Paulo Real Estate Market: Interference of economic indicators in sales of residential Real Estates. International Journal of Real Estate Studies, 10(2).Search in Google Scholar

Moro, M., de Almeida Flores, S., dos Reis, C. C. C., Weise, A. D., & Vincenzi, S. L. (2016b). Previsão de vendas: uma adequação da metodologia ARIMA para o mercado imobiliário de São Paulo. Paper presentation at the Conferência Internacional da Latin American Real Estate Society, São Paulo, Brazil. https://doi.org/10.15396/lares-16-moro_previsao_artigo10.15396/lares-16-moro_previsao_artigoSearch in Google Scholar

Ord, K., Koehler, A., & Snyder, R. (1997). Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models. Journal of the American Statistical Association, 92, 1621–1629. Advance online publication. https://doi.org/10.1080/01621459.1997.1047368410.1080/01621459.1997.10473684Search in Google Scholar

Patton, A., & Sheppard, K. (2009). Optimal combinations of realised volatility estimators. International Journal of Forecasting, 25, 218–238. Advance online publication. https://doi.org/10.1016/j.ijforecast.2009.01.01110.1016/j.ijforecast.2009.01.011Search in Google Scholar

SECOVI-SP. (2015). Na contramão do mercado imobiliário, VCG cresce 155% em 2014. https://incorporacaoimobiliaria.com/tag/mercado-imobiliario/. Acessed 25 jan. 2019.Search in Google Scholar

SECOVI-SP. (2016). Série histórica com a evolução do IVV e do número de unidades ofertadas, vendidas e lançadas em São Paulo. http://www.secovi.com.br/pesquisas-e-indices/pesquisamensal-do-mercado-imobiliario. Acessed 15 jan 2017.Search in Google Scholar

Sehgal, S., & Agarwal, M. (2018). Analogous Examination of Various Machine Learning Algorithm Applied to Big Data. Paper presentation at the International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India. https://doi.org/10.1109/ICACCCN.2018.874885510.1109/ICACCCN.2018.8748855Search in Google Scholar

Shimbo, L. (2013). O aquecimento imobiliário e o mercado de habitação na área central de São Paulo (2001-2010). Revista Latinoamericana de Estudios Urbanos e Regionales., https://doi.org/10.4067/S0250-7161201300020001010.4067/S0250-71612013000200010Search in Google Scholar

Skittides, C., & Fruh, W. (2014). Wind forecasting using Principal Component Analysis. Renewable Energy, 69, 365–374. Advance online publication. https://doi.org/10.1016/j.renene.2014.03.06810.1016/j.renene.2014.03.068Search in Google Scholar

Stock, J., & Watson, M. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23, 405–430. Advance online publication. https://doi.org/10.1002/for.92810.1002/for.928Search in Google Scholar

Temür, S. A., Akgün, M., & Temür, G. (2019). Predicting housing sales in turkey using arima, lstm and hybrid models. Journal of Business Economics and Management, 20, 920–938. Advance online publication. https://doi.org/10.3846/jbem.2019.1019010.3846/jbem.2019.10190Search in Google Scholar

UNITED NATIONS. (2014). World’s population increasingly urban with more than half living in urban areas. http://www.un.org/en/development/desa/news/population/world-urbanizationprospects2014.html?utm_term=United+Nations,+DESA,+Social,+Economic&utm_content=DESA+assists+countries+in+agendasetting+and+decisionmaking+with+the+goal+of+meeting+their+economic,+social+and+environmental+challenges.&utm_source=twitterfeed&utm_medium=twitte. Acessed 18 mar 2019.Search in Google Scholar

Valle dos Santos, R. O., & Vellasco, M. M. (2015). Neural Expert Weighting: A NEW framework for dynamic forecast combination. Expert Systems with Applications, 42, 8625–8636. Advance online publication. https://doi.org/10.1016/j.eswa.2015.07.01710.1016/j.eswa.2015.07.017Search in Google Scholar

Varandas Júnior, J., Alencar, C. (2009). Modelos de projeção de demanda aplicado ao mercado imobiliário. Paper presentation at Conferência Internacional da Latin American Real Estate Society, São Paulo, Brazil.Search in Google Scholar

Wang, L., Wang, Z., Qu, H., & Liu, S. (2018). Optimal Forecast Combination Based on Neural Networks for Time Series Forecasting. Applied Soft Computing, 66, 1–17. Advance online publication. https://doi.org/10.1016/j.asoc.2018.02.00410.1016/j.asoc.2018.02.004Search in Google Scholar