نوع مقاله : علمی - پژوهشی
1 دانشجوی دکتری، دانشگاه صنعتی شریف، تهران، ایران
2 استادیار، دانشگاه صنعتی شریف، تهران، ایران
عنوان مقاله [English]
This study explores multiple mathematical models for estimating house prices in Tehran, Iran. Estimation of house prices is vital from various perspectives. Estimated prices can be used in the planning, design, and construction of residential building projects. Based on the estimated future prices of residential units, investors and constructors can conduct an investment analysis and mitigate the risk of the financial failure of residential projects. Reducing the investment risk of residential projects can lead to jobs creation and economic growth. Several factors such as previous house prices, population changes, house construction costs, and seasonal effects can significantly affect house prices. In this research, first, the macro and microeconomic factors that affect house prices were reviewed, and relevant data were collected. The next step was data preparation and pre-processing. Then, the data was used to train several models using regression and time series analysis models, namely Autoregressive Integrated Moving Average and Vector Autoregression. The current house prices can be estimated using the regression model based on a set of independent variables such as the age of the building. The ARIMA model receives the output of the regression model and estimate house price in the subsequent year. Alternatively, the Vector Autoregression model can be used independently to estimate future prices. To compare the performance of the models, their error was measured by two methods: Mean Absolute Percentage Error and Relative Standard Error. The error of the Autoregression model is less than the combination of regression and ARIMA models because in the Autoregression model, the effect of independent variables is directly applied in the model. The models developed in this research can help decision-makers, investors, developers, homebuyers, and financial institutes obtain appropriately estimated prices and make informed decisions in construction projects.