Forecasting of the U.S. House Price Index: A Comparative Study of ARIMA, SARIMA, and ETS Models
DOI:
https://doi.org/10.54097/a5bgqr58Keywords:
Prediction; ARIMA; SARIMA; ETS; CSUSHPINSA.Abstract
The U.S. housing price index (CSUSHPINSA) is an important measure of the real estate market. It reflects overall economic development, financial market activity, and changes in consumer purchasing power. This makes it valuable for research and forecasting. In this study, monthly data from January 1987 to December 2020 are used as the training set, and data from January 2021 to June 2025 are used as the testing set. Time series models are built to forecast the U.S. housing price index. Three models are applied: ARIMA, SARIMA, and ETS. They are evaluated through parameter estimation, model fitting, and forecasting comparison. Their performance is assessed in terms of trend description, interval coverage, and forecasting accuracy. Results show that all three models continue the long-term upward trend of the housing price index. ARIMA underestimates growth during the testing period, while ETS gives wide intervals and means forecasts that deviate from the actual values. In contrast, SARIMA produces forecasts that match the observed trend closely. Its error measures, including RMSE, MAE, and MAPE, are much lower than those of the other models. In conclusion, the SARIMA model not only reflects the long-term trend but also captures seasonal fluctuations effectively. It is the most suitable forecasting method in this study and provides useful reference value for housing market monitoring and policy making.
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[1] Case K E, Glaeser E L, Parker J A. Real estate and the macroeconomy. Brookings Papers on Economic Activity, 2000(2): 119-162. DOI: https://doi.org/10.1353/eca.2000.0011
[2] Case K E, Quigley J M, Shiller R J. Comparing wealth effects: the stock market versus the housing market. Topics in Macroeconomics, 2005, 5(1). DOI: https://doi.org/10.2202/1534-6013.1235
[3] Goodhart C, Hofmann B. House prices, money, credit, and the macroeconomy. Oxford review of economic policy, 2008, 24(1): 180-205. DOI: https://doi.org/10.1093/oxrep/grn009
[4] Benjamin J, Chinloy P, Jud D. Why do households concentrate their wealth in housing. Journal of Real Estate Research, 2004, 26(4): 329-344. DOI: https://doi.org/10.1080/10835547.2004.12091146
[5] Ngai L R, Sheedy K D. The Ins and Outs of Selling Houses: Understanding Housing-Market Volatility. International Economic Review, 2024, 65(3): 1415-1440. DOI: https://doi.org/10.1111/iere.12693
[6] Kohler K, Tippet B, Stockhammer E. House price cycles, housing systems, and growth models. European Journal of Economics and Economic Policies, 2023, 20(3): 461-490. DOI: https://doi.org/10.4337/ejeep.2023.0121
[7] Ryan-Collins J. Breaking the housing–finance cycle: Macroeconomic policy reforms for more affordable homes. Environment and planning A: economy and space, 2021, 53(3): 480-502. DOI: https://doi.org/10.1177/0308518X19862811
[8] Agnello L, Castro V, Sousa R M. The housing cycle: what role for mortgage market development and housing finance. The Journal of Real Estate Finance and Economics, 2020, 61(4): 607-670. DOI: https://doi.org/10.1007/s11146-019-09705-z
[9] Haffner M E, Hulse K. A fresh look at contemporary perspectives on urban housing affordability. International Journal of Urban Sciences, 2021, 25(sup1): 59-79. DOI: https://doi.org/10.1080/12265934.2019.1687320
[10] Anthony J. Housing affordability and economic growth. Housing policy debate, 2023, 33(5): 1187-1205. DOI: https://doi.org/10.1080/10511482.2022.2065328
[11] Mohammed J K, Aliyu A A, Dzukogi U A, et al. The impact of COVID-19 on housing market: A review of emerging literature. International Journal of Real Estate Studies, 2021, 15(2): 66-74. DOI: https://doi.org/10.11113/intrest.v15n2.128
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