We augment linear pricing models for the housing market commonly used in the literature with google trends data in order to assess whether or not crowd-sourced search query data can improve the forecasting ability of the models. We compare various performance measures of the augmented linear...

We characterize the relationship between the rent-to-price ratio and house market returns and rent growth in a model where the demographic variable, the middleaged to young ratio, captures the slowly evolving components in the rent-to-price ratio. To document the linkage between demographics and...

This study contributes a house price prediction model selection in Tehran City based on the area between Lorenz curve (LC) and concentration curve (CC) of the predicted price by using 206,556 observed transaction data over the period from March 21, 2018, to February 19, 2021. Several different...

This paper examines whether machine learning (ML) algorithms can outperform a linear model in predicting monthly growth in Canada of both house prices and existing home sales. The aim is to apply two widely used ML techniques (support vector regression and multilayer perceptron) in economic...

This study investigates the effects of key economic factors on the median list price and median selling price in the U.S. housing market. Key economic factors such as interest rates, unemployment rates, inflation rates, real gross domestic product, money supply, mortgage rate, Standard & Poor's...