Forecasting the price of residential units in District 5 of Tehran Munici-pality, considering the fluctuations of the currency market

Housing prices are one of the indicators that identify the factors affecting it and could help to increase the efficiency of plans and to present housing planning strategies and policies. Despite many exchange rate fluctuations in recent years, there is a need to create a model that pays attention to the economic factors affecting housing prices in addition to the ordinary housing features. Since the housing price modeling is one of the issues that has a spatial component, therefore, in presenting the model related to housing prices, its location should also be considered. Therefore, in this study, the analysis of the spatial distribution of housing prices in district 5 of Tehran municipality and the factors affecting that have been investigated. In this regard, housing sales data in this region in 2018, 2019, and 2020 have been used to model housing prices. The research results have been obtained by the Multiscale Geographically Weighted Regression (MGWR) method, which provided better results compared to those by both the Geographically Weighted Regression (GWR) and Ordinary Least Squares (OLS) methods. The adjusted coefficient of determination in OLS, GWR, and MGWR algorithms was obtained equal to 0.762, 0.821, and 0.853, respectively. The MGWR method is one of the methods that can model the spatial heterogeneity of housing price data. According to the results, the exchange rate variable (dollar price) has the greatest impact on housing price modeling.