The issue of property evaluation and appraisal has been of high interest for private and public agents involved in the housing industry for the purposes of trade, insurance and tax. This paper aims to investigate how different factors related to the location of a property affect its price over time. The predictive models applied in this research are driven by real estate transactions data of Tehran Metropolitan Area, captured from open data available to the public. The parameters of the functions that describe the behavior of the housing market are estimated through applying different types of statistical models, including ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR). This suite of models has been run in order to compare their efficiency and accuracy in predicting the variations in housing price. The GTWR model showed significantly better performance than OLS and GWR models, as the goodness of fit index (adjusted R²) improved by 22 percent. Therefore, spatio-temporal non-stationary modelling is significant in the explanation of the variations in housing value and the GTWR coefficients were found more reliable. Three internal factors (size of building; building age; building quality), and eight external factors (topography; land-use mix; population density; distance to city center; distance to subway station; distance to regional parks; distance to highway; distance to airport) influence the property price, either positively or negatively. Moreover, using significant variables that extracted from regression models, the optimum number of housing value clusters is generated using the spatial ‘k’luster analysis by tree edge removal (SKATER) method. Five clusters of housing patterns were recognized. The policy implication of this paper is grouping of Metropolitan Tehran housing value data into five clusters with different characteristics. The varying factors influencing housing value in each cluster are different, making this data analysis technique useful for policy-makers in the housing sector.

对于从事住房行业的私人和公共机构而言，出于贸易，保险和税收的目的，财产评估和评估问题引起了人们的极大兴趣。本文旨在研究与房产位置相关的不同因素如何随时间影响其价格。本研究中使用的预测模型由德黑兰都会区的房地产交易数据驱动，该数据是从公开的公开数据中获取的。通过应用不同类型的统计模型（包括普通最小二乘（OLS），地理加权回归（GWR）和地理和时间加权回归（GTWR））来估算描述住房市场行为的函数参数。运行该模型套件是为了比较它们在预测住房价格变化时的效率和准确性。GTWR模型显示出比OLS和GWR模型显着更好的性能，因为拟合指数（调整后的R²）提高了22％。因此，时空非平稳建模对于解释房屋价值的变化非常重要，并且GTWR系数更加可靠。三个内部因素（建筑物的大小；建筑物的年龄；建筑物的质量），以及八个外部因素（地形，土地使用的结构；人口密度；到市中心的距离；到地铁站的距离；到区域公园的距离；到高速公路的距离；距离到机场）正面或负面影响房地产价格。此外，使用从回归模型中提取的重要变量，可使用通过树边缘去除（SKATER）方法进行的空间“ k”光泽分析来生成最佳数量的房屋价值集群。确认了五类住房模式。本文的政策含义是将德黑兰大都会房屋价值数据分组为五个具有不同特征的集群。影响每个群集中房屋价值的不同因素是不同的，这使得此数据分析技术对房屋部门的决策者很有用。