<abstract xmlns="http://www.w3.org/1999/xhtml">

The Turkish Housing Market has experienced a steep increase in prices. Individual and corporate investors now possess tools to estimate the real estate evaluation while using smaller amounts of data with traditional techniques. Not having an analytical approach to evaluate the price of real estate could cause the investor to lose considerable amounts of money, especially in the case of individual investors. This study aims to determine how different machine learning algorithms with real market data can improve this process.
To be able to test this, over 30000 lines of housing market data with over 13 variables is scraped. Data is cleansed, manipulated and visualized, while predictive models such as linear regression, polynomial regression, decision trees, random forests, and XGboost are created and compared according to the CRISP-DM framework. The results show that using complex techniques to create machine learning models could improve the accuracy in predicting the listing prices of houses.
This paper aims to:
– analyze the effects of using a real and relatively large amount of data,
– determine the main variables that contribute to the evaluation of an estate,
– compare different machine learning models to find the optimal one for the real estate market,
– create an accurate model to predict the value of any house on the Istanbul market.
</abstract>

The Turkish Housing Market has experienced a steep increase in prices. Individual and corporate investors now possess tools to estimate the real estate evaluation while using smaller amounts of data with traditional techniques. Not having an analytical approach to evaluate the price of real estate could cause the investor to lose considerable amounts of money, especially in the case of individual investors. This study aims to determine how different machine learning algorithms with real market data can improve this process.
To be able to test this, over 30000 lines of housing market data with over 13 variables is scraped. Data is cleansed, manipulated and visualized, while predictive models such as linear regression, polynomial regression, decision trees, random forests, and XGboost are created and compared according to the CRISP-DM framework. The results show that using complex techniques to create machine learning models could improve the accuracy in predicting the listing prices of houses.
This paper aims to:
– analyze the effects of using a real and relatively large amount of data,
– determine the main variables that contribute to the evaluation of an estate,
– compare different machine learning models to find the optimal one for the real estate market,
– create an accurate model to predict the value of any house on the Istanbul market.

Real Estate Market Price Prediction Model of Istanbul

Real Estate Management and Valuation

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Real Estate Market Price Prediction Model of Istanbul"}

The Turkish Housing Market has experienced a steep increase in prices. Individual and corporate investors now possess tools to estimate the real estate...

Real Estate Market Price Prediction Model of Istanbul

Data publikacji: 09 gru 2022

Zakres stron: 1 - 16

DOI: https://doi.org/10.2478/remav-2022-0025

Słowa kluczowe
price prediction, machine learning, real estate market

© 2022 Mert Tekin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Real Estate Market Price Prediction Model of Istanbul

Data publikacji: 09 gru 2022

Zakres stron: 1 - 16

DOI: https://doi.org/10.2478/remav-2022-0025

Słowa kluczoweprice prediction, machine learning, real estate market

© 2022 Mert Tekin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
price prediction, machine learning, real estate market