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.

Breakthrough changes in the rental market have occurred with the introduction of peer-to-peer accommodation services such as Airbnb. This phenomenon is attracting tourists who contribute to the sustainability of local trade and the economic development of the city. This research enriches the current debate on the range of factors that influence Airbnb accommodation prices. To that end, a method was developed to understand the relationship between Airbnb accommodation attributes and listing prices; and to consider variables related to the properties’ location and surrounding urban environment, considering the touristic characteristics of the four Spanish Mediterranean Arc cities selected as case study. A multivariable analysis technique is used for estimating a hedonic price model that adopts the ordinary least squares and the quantile regression methods. The findings obtained for the impact of location on listing prices are contrary to previous studies. In fact, accommodation prices increase incrementally by 1.3% per kilometer from the tourist area, which in all four cases are situated in the historic area of the city. However, at the same time, accommodation prices decrease incrementally as distance from the coastline increases. Lastly, results related to how the listings’ accommodation, host, and advertising characteristics impact Airbnb prices concur with previous studies.

The existence of a green premium in house (asking) prices in Alicante province, Spain, are analyzed using circa 9000 property observations. In developing the sample, information from energy efficiency certificates was matched with two other databases. The model tests for green premium by climatic zones using pool Ordinary Least Squares (pool-OLS) and Instrumental Variables (IV) hedonic models, adds new knowledge concerning the existence of green premiums from Southern Europe, explores differences in their estimation by climatic zone, debates the nature of the estimated green parameters, and explains the role of endogeneity in hedonic green premium models. The empirical evidence assesses the sensitivity of asking price to either energy consumption (KWh) or carbon dioxide emissions (CO₂) with an apparent premium of 3%, and captures an association with efficiency rating from G to F of 1.8% and from F to E of 1.1%. Significantly, the results relating to price responses show a distinct variation between the coast and the cooler climatic zone of the interior. The paper shows that energy efficiency incentive policies should discriminate by climatic areas, and provides a price reference by which to assess the amount of incentives needed to achieve European Union (EU) objectives.

This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that purpose, a comparison is made between location patterns of geolocated data from various social networks (i.e., Google Places, Foursquare, Twitter, Airbnb and Idealista) and statistical information such as land value, average gross income, and population distribution by age range. The main findings show that there is no direct relationship between land value or age of registered population and the amount of social network data generated in a given area. However, a noteworthy coincidence was observed between Google Places data-clustering patterns, which represent the offer of economic activities, and the spatial concentration of the other LBSNs analyzed, suggesting that data from these sources are mostly generated in areas with a high density of economic activities.

Studies have found that housing with energy performance certificates have a positive premium in sales price. However, other studies have obtained negative or unexpected results. The objective of this study is to determine whether or not housing with energy performance certificates (EPC) have positive premiums in the sales price. For this purpose, a systematic review, meta-analysis, and meta-regression of prior studies were conducted in order to determine whether the existence of an EPC influences sales price. A total of 66 documents were examined, with a total of 173 sales registers. The impact of having or not having an EPC was analyzed for housing sales price premiums on a global level, as well as the premiums in Europe for each of the ABCDEFG qualification letters. The results suggest that: 1) Globally speaking, it is estimated that housing with an EPC has an overall price premium of 4.20%, on a continent level, with premiums of 5.36% being obtained in North America, 4.81% in Asia, and 2.32% in Europe; 2) in Europe, the results are not conclusive with regards to the ABCDEFG qualification, since there is no consensus as to the letter base to be used as a reference for comparisons, thereby generating small comparable samples.

After almost a decade of crisis, the housing market in Spain shows significant signs of recovery, with increases in both the average price and the number of sales transactions. Housing is the main asset for the majority of households, and it also has the most resources devoted to it, thus, when it comes to buying a residence, people do not only look at the asset’s intrinsic characteristics, but also consider other particularities such as the neighbourhood, accessibility to services, availability of public transport or adequate funding. The study aimed to analyse and quantify the relationship that exists between the asking price of second-hand housing on the market in Alicante and the attributes that characterise them. This was done using a multivariate analysis to estimate a hedonic pricing model by ordinary least squares and a quantile regression to analyse the impact of the characteristics in different price ranges. The results show the segmentation of the prices in the Alicante market, with higher prices in the northern coastal area over the southern and inland comarcas.