Abstract
Precise prediction of housing values is an important task for various stakeholders involved in the housing market, including investors, builders, and city planners. In this research, supervised machine learning models are used to predict the price of apartments in Almaty, Kazakhstan, which is a dynamic urban market in Central Asia. With an openly available dataset of apartments for sale, Linear Regression, Lasso Regression, Random Forest, and XGBoost models are implemented and tested. The data is scaled and encoded with scalable pipelines, and models are evaluated with regards to Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² Score. The best performing model amongst those tested was Random Forest Regressor with an R² of 0.9158, followed by XGBoost with 0.8438. Feature importance visualization identifies district, area, and construction year as primary influencing factors. The research supports that ensembling machine learning models are efficient and scalable predictors for housing forecasts and suggests future improvements with time-series and geospatial features