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House Value Forecasting Based on Time Series

Ahmadi, Shahrzad | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53379 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Shavandi, Hassan; Khedmati, Majid
  7. Abstract:
  8. Making money and maintaining the value of assets has always been one of the most important concerns of people. Real estate is one of the essential human needs, but it is also considered an investment tool for individuals. In addition to individuals in a family, various groups and organizations such as policymakers, analysts, banks and financial institutions, taxpayers, and real estate investors are directly or indirectly affected by the dynamic characteristic of the housing market. Therefore, forecasting the exact amount of housing value in the future is very important. Factors that can improve this forecasting's accuracy include considering the relationship between housing value and macroeconomic variables, as well as the impact of regional housing markets on each other and the ripple effect. In this study, different time series models have been used to forecast housing value in Los Angeles. For this purpose, houses are classified into five groups based on the number of bedrooms, and a time series is provided for each of them. In the first stage, ARIMA model, VAR model with the effect of three areas included Los Angeles, Santa Barbara, and San Luis Obispo, and VAR model with the effect of five housing series based on the number of bedrooms, have been forecasted. In the second stage, these models are implemented in SSM format, and finally, macroeconomic variables are added to the models. Comparing the performance of the models with each other, it is concluded that for each of the five Los Angeles housing series, first the ARIMA-SSM model with macroeconomic variables and then the VAR-SSM model with macroeconomic variables have the best performance in out of sample forecasting. Other models have performed differently in different series
  9. Keywords:
  10. Forecasting ; Macroeconomic Variables ; Housing Value ; Time Series ; Vector Autoregressive Model ; Autoregressive Integrated Moving Average (ARIMA) ; State Space

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