The influence of housing location on energy ratings price premium in Alicante, Spain

Céspedes-López, María Francisca; Perez-Sanchez, V. Raul; García, Raúl Tomás Mora

doi:10.1016/j.ecolecon.2022.107579

Cited by 2 publications

(1 citation statement)

References 66 publications

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“…The unit with which each variable has been measured is also indicated, as well as a brief description of the same. The selection of the features used was based on the literature review of other research, the availability of data and the previous experience of other research conducted in the Alicante real estate market [58][59][60][61].…”

Section: Study Area Information Sources and Databasementioning

confidence: 99%

Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times

García

Céspedes-López

Perez-Sanchez

2022

Land

Self Cite

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Machine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithms to analyze and identify patterns, as well as to make predictions that help users in decision making. This research aims to identify the best machine learning algorithms to predict house prices, and to quantify the impact of the COVID-19 pandemic on house prices in a Spanish city. The methodology addresses the phases of data preparation, feature engineering, hyperparameter training and optimization, model evaluation and selection, and finally model interpretation. Ensemble learning algorithms based on boosting (Gradient Boosting Regressor, Extreme Gradient Boosting, and Light Gradient Boosting Machine) and bagging (random forest and extra-trees regressor) are used and compared with a linear regression model. A case study is developed with georeferenced microdata of the real estate market in Alicante (Spain), before and after the pandemic declaration derived from COVID-19, together with information from other complementary sources such as the cadastre, socio-demographic and economic indicators, and satellite images. The results show that machine learning algorithms perform better than traditional linear models because they are better adapted to the nonlinearities of complex data such as real estate market data. Algorithms based on bagging show overfitting problems (random forest and extra-trees regressor) and those based on boosting have better performance and lower overfitting. This research contributes to the literature on the Spanish real estate market by being one of the first studies to use machine learning and microdata to explore the incidence of the COVID-19 pandemic on house prices.

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Section: Study Area Information Sources and Databasementioning

confidence: 99%

Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times

García

Céspedes-López

Perez-Sanchez

2022

Land

Self Cite

View full text Add to dashboard Cite

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Energy Price Fluctuation and Real Estate Market Risk Prevention—Empirical Evidence at the Prefecture-Level City Level in China

Wang,

Yan,

Wang

2024

China Finance and Economic Review

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The impact of energy price fluctuation on the macro economy is a hot issue that is continuously concerned by the academic community, but few literature have paid attention to the impact of energy price fluctuation on housing price and the financial risks that may arise. Based on the data of cities at and above the prefecture level in China, this paper empirically examines the impact of energy price fluctuation on the price fluctuation of the domestic real estate, and analyzes its influencing mechanism and transmission pathways. The results show that changes of energy price will significantly affect the fluctuation of domestic real estate price, and it is more obvious in large cities and megacities. Energy price affect the demand for housing through changes of interest rate levels on the one hand, and the supply of the real estate market through the cost of housing construction on the other hand, which leads to housing price fluctuation. After a series of robustness tests, the results are still valid. At the same time, the effect is asymmetric, that is, higher energy price increases the fluctuation of house price, but the impact of falling energy price is not significant. Therefore, when preventing real estate market risks, energy price should be considered as a forward-looking indicator to focus on, and the regulation and control policies of the real estate market should be scientifically formulated. This paper not only provides a new perspective on the mechanism of housing price formation, but also enriches the research on the interconnection between energy and financial markets.

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The influence of housing location on energy ratings price premium in Alicante, Spain

Cited by 2 publications

References 66 publications

Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times

Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times

Energy Price Fluctuation and Real Estate Market Risk Prevention—Empirical Evidence at the Prefecture-Level City Level in China

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