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Article

Are Cave Houses a Sustainable Real Estate Alternative?

by
Maria-Francisca Cespedes-Lopez
* and
Raul-Tomas Mora-Garcia
*
Building Sciences and Urbanism Department, University of Alicante, 03690 San Vicente del Raspeig, Spain
*
Authors to whom correspondence should be addressed.
Land 2022, 11(12), 2185; https://doi.org/10.3390/land11122185
Submission received: 12 November 2022 / Revised: 25 November 2022 / Accepted: 25 November 2022 / Published: 1 December 2022
(This article belongs to the Special Issue Urban Regeneration and Local Development)
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Abstract

:
The high level of energy consumption of buildings has outlined the need for more sustainable and environmentally friendly constructions, which has led to cave houses now being more highly valued. This study looks to study whether sustainable constructions, such as cave houses, have an economic impact with regard to other construction types in the real estate market in Granada. Moreover, this study looks to determine whether energy rating is a relevant characteristic for the price of cave houses and whether the price determinants vary according to whether the house in question is a cave house or a single-family one. To develop this study, a final sample of 61,573 properties was used. A regression model estimated by ordinary least squares was performed. This study shows that cave houses are being marketed at higher prices than single-family houses. It was noted that energy rating is not an important characteristic for estimating the price of a cave house. Finally, in this type of housing, refrigeration equipment is not one of the determining characteristics for the price.

1. Introduction

In the history of architecture, caves were man’s first refuge or house, and, despite now being in disuse, they were once one of the most widespread construction types in the world. Housing, since its beginnings, has been evolving and adapting throughout the centuries according to the human and social needs of the time; structures were made with local materials and techniques and were adapted to the local climate. Both the industrial revolution and the huge demographic explosion caused excessive urban development [1], which resulted in the current worldwide problem of high energy consumption in buildings, being above both the transport and industry sectors [2]. This consumption is mainly due to the demand for electricity needed for a heating, ventilation, and air-conditioning (HVAC) system and the use of household appliances [3].
The main result of this has been the implementation of politics that favor the improvement of energy efficiency in the European Union (EU) [4,5,6,7], which have been integrated into a series of decrees in Spain [8,9]. As a result, there is a need to look for more sustainable and environmentally friendly construction solutions.
Cave houses are considered to be a sustainable housing typology as they are built using a construction type that utilizes the thermal inertia of the land. As a result, the interior temperature of the house scarcely changes throughout the different seasons of the year, which allows for thermal comfort to be ensured in the interior of the house without the need for additional energy inputs (air-conditioning or heating) [10,11]. Cave houses are also considered sustainable because of the combination of social, economic, and cultural factors [12,13,14].
There are different studies on cave houses (Figure 1a) that address different lines of research, such as: (1) the importance of preserving and maintaining architectural heritage [15,16]; (2) the importance of assessing the application of bioclimatic strategies used for cave houses in present-day constructions [17]; (3) the importance of analyzing thermal comfort or whether the ventilation in cave houses is adequate; [10,18,19,20,21,22,23,24,25]; and (4) the psychological and physiological problems associated with this type of construction [26,27].
Moreover, several studies have been carried out in which it has been noted that the buyers of single-family houses (Figure 1b) are willing to pay more if a house is more efficient [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42]. A broader overview regarding housing prices and energy ratings can be found in the work by [43,44].
Cave houses are excavated in the rock itself, normally with vaulted forms for a better transmission of the upper loads, and may or may not have attached constructions. They are made up of one or more cavities where the usual activities and living and sleeping are carried out. The walls and vaulted ceilings are finished with a lime coating and the most rustic pavements are made with the ground itself or with a layer of cement. As a consequence of the current housing needs, these constructions have been adapted and improved, increasing the number of interior spaces and improving the qualities and facilities.
The single-family houses are characterized by having a structure based on load-bearing walls or reinforced concrete structures. They are usually distributed in a living–dining room, kitchen, bathroom and bedrooms. The most common materials used are ceramic bricks covered with plaster for the vertical partitions; the floors are usually made of natural or artificial stone, ceramic tiles or wood; and the ceilings can be made of plaster or plasterboard.
Vernacular architecture is one of the most widespread types of architecture in Spain and is specifically used for housing. Authors such as [45,46,47,48] have confirmed that society has changed its attitude towards cave houses and considers this type of construction to be sustainable. However, has this change in attitude been reflected in the asking price of this type of construction? As far as the authors are aware, there have been no studies that analyze the prices of cave houses. The lack of results in the literature has resulted in new research questions. Firstly, as cave houses are considered sustainable, does this mean that they are marketed at higher prices than single-family houses? Secondly, is energy rating a relevant characteristic for cave houses? Thirdly, if the interior of a cave house is maintained at a constant temperature throughout the year, would an HVAC system be a price-determining feature?
The main objective of this study was to determine the economic impact that cave houses have with regard to single-family houses in the real estate market in the province of Granada (Spain). The secondary objective of this study was to understand whether energy rating and HVAC systems are relevant characteristics when determining the price of a cave house.
The first hypothesis that was put forward for the study (H1) was that cave houses are being marketed at higher prices than single-family houses. The second hypothesis that was proposed (H2) was that energy ratings for cave houses are a relevant characteristic. The third hypothesis generated (H3) was that the availability of an HVAC system is not a determining characteristic for the price of cave houses. However, it is for single-family houses.
The estimates made from the regression model showed that cave houses are marketed at higher prices than single-family houses. It was also observed that energy ratings and the availability of air-conditioning are not determining characteristics for the price of cave houses.
This document is organized as follows: the first section includes the introduction, objectives and hypotheses. The second section denotes the materials and the methods used, outlining the sources that were employed and the database generated. The third section details the results. The fourth section denotes the discussion, and finally, Section 5 explains the conclusions obtained.

2. Materials and Methods

To understand what characteristics determine the asking price of a house, an ordinary least squares (OLS) hedonic regression model was performed. This type of analysis began to be used with the “New Consumer Theory” developed by Lancaster [49]. Later, Ridker and Henning [50] used the regression model in the housing market for the first time. Authors such as Zietz et al. [51] noted that a hedonic regression analysis is typically used to identify the marginal effect that a set of characteristics has on housing prices. In terms of heterogeneous goods such as housing, the hedonic methodology allows for the effect of each characteristic on the price to be estimated [52]. Currently, this is one of the most used methodologies for determining the economic premium generated by different characteristics.

2.1. Population and Sample

The database consisted of cave houses and single-family houses offered for sale in the province of Granada (Andalusia). This province is located in the south of Spain and has 10 regions or comarcas 1 (Figure 2a).
The interest and selection criteria for this territory were due to the fact that cave houses are a traditional construction in Spain. The province of Granada and the province of Almería have the highest concentration of cave houses and have developed them the most in the country [45,53] (Figure 2b).
The representativeness of the sample was verified using the equation for large or infinite populations when the exact size of the component units is unknown [54]. Using a confidence level of 95% (zα/2 = 1.96), a probability level of p = 0.50 and a sample size of N = 61,573, a maximum error of 0.39% (0.0039) was estimated, which guaranteed a high statistical precision of the sample.
The main information sources were two real estate portals. The first was idealista, which was used between June 2017 and March 2018, and the second was habitaclia, which was used between June 2019 and January 2021. During these time periods, information on the houses offered was collected, and data about the characteristics of the houses were extracted. A geographic information system (GIS) was then used to provide the location and neighborhood information from other sources of information. Data from the population and housing census tract by the National Institute of Statistics were used [55]. Each house was assigned information about the type of occupation (vacant, main residence and second residence) as well as the socio-demographic characteristics of the population (dependency, aging, foreign population and level of education).
The research had several limitations. Firstly, a limitation was the lack of information from official sources about the real transaction prices and housing characteristics. Another limitation was the possible errors in the information or the omission of information on the prices and/or characteristics of the houses that were offered on the two real estate portals.
With regard to the first limitation, authors such as [56,57,58,59] suggest that real estate asking prices are an adequate substitute for transaction prices. Moreover, there are studies that analyze the effect of the housing characteristics on the price, using listing price information from real estate portals due to the lack of information from other official sources [35,39,60,61,62,63,64].
In terms of the second limitation, the data was preprocessed so that properties with missing data could be identified and discarded. As a result, 3030 properties were discarded. To identify the atypical values in the quantitative variables, a limit of three standard deviations was established for the set of values [65,66]. As a result, 595 properties were discarded. The final sample consisted of 61,573 properties.

2.2. Data

To identify the most important variables in the estimate of the single-family house prices, a literature review was carried out, which is shown in Table 1.
Based on the information received, 40 variables were obtained, as summarized in Table 2. The variables were ordered according to three categories. House characteristics (A); Location characteristics (B); and Neighborhood characteristics (C). The unit with which each variable was measured, a brief description of the variable and the descriptive statistics are indicated.
Figure 3 shows the location of the cave houses (Figure 3a) and single-family houses (Figure 3b) analyzed within the territory. Regarding the distribution of prices, it can be noted that in the center of Granada and on the coast, unit prices (€/m2) are higher than in the rest of the regions of the province (Figure 3c,d). At the same time, it can also be noted that there is a higher percentage of cave houses with high ratings (letters A, B and C) with respect to the sample of single-family houses (Figure 3e,f).

2.3. Methodology

The regression model was estimated by ordinary least squares (OLS) with a semi-logarithmic functional form, as this facilitated the interpretation of the coefficients as average percentage premiums. Moreover, this reduced problems of heteroscedasticity and improved the goodness of fit of the data [58,93]. The model used had the following expression:
l n P i = α + ∑ j = 1 n β j X i j + ∑ k = 1 m γ k D i k + ε i
where:
  • ln (Pi) is the natural logarithm of the advertised asking price for the house “i”;
  • α is the fixed component, it does not depend on the market;
  • βj is the parameter to estimate related to the characteristic “j”;
  • Xij is the continuous variable that collects the characteristic “j” of the observation “i”;
  • Yk is the parameter to estimate related to the characteristic “k”;
  • Dik is the dummy variable that collects the characteristic “k” of the observation “i”;
  • εi is the error term in the observation “i”.
The model was estimated three times. The first estimate was for the complete set of observations. The other two estimates corresponded to the subsamples according to whether the houses were cave houses or single-family houses. This was in such a way that the results obtained made it possible to compare the determining characteristics for each group. The SPSS for Windows version 26 IBM Corp. statistical package [94] was used to perform this analysis, using the listwise deletion method.

3. Results

Regression Analysis

To determine whether the estimates reach the adequate level of quality, the following checks were carried out: the normality of the population; ensuring that there are no specification problems in the estimates (no multicollinearity, heteroscedasticity or autocorrelation); the statistical significance of the estimates; and, finally, whether the proportion of variance estimated is high (R2).
The normality of the population was tested through the use of a histogram (Figure 4a) and a normality plot of the residuals (Figure 4b), which showed that the sample is a normal distribution. Multicollinearity was verified by using the VIF statistic (variance inflation factor). Many authors suggest that collinearity problems exist if any VIF is greater than 10 [95,96]. In the three estimates carried out, the majority of the VIF values are found to be between 1 and 3.5, which leads to the conclusion that there are no problems due to multicollinearity. Heteroscedasticity is analyzed with a scatter plot of the residuals (Figure 4c), and no serious problems of heteroscedasticity are found, showing a random distribution of the residuals. The existence of autocorrelation is checked with the Durbin–Watson statistic, obtaining the closest values to two in all the estimates, suggesting the absence of autocorrelation in the residuals [97,98]. The significance of each estimate is measured with the Snedecor’s F test and is statistically significant. The coefficient of determination (adjusted R2) of the estimates is shown in Table 3. and an explanatory power of 69% is obtained for estimates 1 and 3 and 81% for estimate 2. In summary, it can be said that the estimates reach a sufficient level of robustness and statistical significance to make them acceptable for inference.
The results of the different estimates of the regression model are shown in Table 3. The results obtained for estimate 1 of the model are described, as they correspond to the complete sample. With respect to the housing characteristics, the model estimates that increasing one square meter of floor space leads to a price increase of 0.2%, while the inclusion of an additional bedroom, bathroom or toilet leads to an increase of 2.6%, 15.7% and 10.8%, respectively. The average price impact of a house including extras such as air-conditioning, heating, built-in closets or being sold furnished was estimated by the model as having an increase of 5.3%, 11.4%, 6.7% and 7.1%, respectively. A house being a new construction (less than 3 years) leads to an increase in the price of 6.9%. The values obtained in the estimate of the parameters related to other characteristics, such as a house having a garage, garden or pool, lead to an average price increase of 8.1%, 9.7% and 19.8%, respectively. If the house is a cave house, the average asking price is 5.3% higher than that of a single-family house.
With regard to energy rating, houses with an E letter rating are taken as a reference. The results show that houses with a high letter rating (letters A and B) have higher economic premiums than other houses with worse ratings or those that do not advertise them (with some of these coefficients being non-significant).
Regarding location, the estimated impact for the price of the houses located in the Costa Granadina comarca is an increase of 21.1% in relation to the reference region, which is the Vega de Granada comarca. For the other comarcas, the model estimates a decrease in the asking prices offered, reaching reductions of between 17.4% and 54.5% with respect to the Vega de Granada comarca.
In terms of the neighborhood characteristics, a 1% increase in the dependency ratio and aging ratio leads to a price increase of 0.15% and 0.04%. Regarding the percentage of foreigners or people with university-level studies, the model estimated that an increase of 1% in these variables leads to a price increase of 0.7% and 1.8%, respectively. On the contrary, an increase in the percentage of uneducated people leads to a reduction of 0.3%.
Regarding the type of housing occupancy, the model shows price reductions of 0.1% and 0.04% respectively, in areas with a higher percentage of secondary residences or vacant houses.
In the estimates of the subsamples (estimates 2 and 3), some differences are observed (Figure 5 and Table 3). In the estimate of subsample 2 (cave houses), with regard to the characteristics of the house, only surface area, number of bedrooms and bathrooms, availability of heating, fireplace, furniture in the house and parking have positive and significant premiums. Other variables are not significant, such as the number of toilets, the availability of air-conditioning, built-in closets, a garden and a swimming pool. With regard to the energy rating, all the variables are non-significant, which may highlight that this characteristic is not relevant in this market segment. In terms of location, there are four comarcas that do not have a sufficient number of observations and are consequently discarded from the estimate. Using the Vega de Granada comarca as a reference, the houses situated in Baza, Guadix and Huéscar have an average price reduction of between 46.4% and 77.9%. In terms of the neighborhood characteristics, only the variables regarding the aging index, foreigners and those with university studies are significant. Finally, the occupation-type variables are not significant.

4. Discussion

Regarding the first objective, the aim is to determine the economic impact of cave houses, as opposed to single-family houses, and, as such, the first hypothesis—H1—is proposed. Estimate 1 confirms this hypothesis since cave houses are marketed 5.3% more expensive than single-family houses. This may be due to several factors. It is possible that this is due to a greater interest in exploiting these houses for short-term rental, as can be seen in platforms such as Booking or Airbnb, with a high supply of these homes and tourist resorts in prominent areas such as Huéscar, Guadix, Baza or Granada. This tourist interest was already highlighted by Urdiales [45] in 2003, being a movement initiated in the 1990s and mainly led by foreign promoters. Another main factor may be a consequence of foreign buyers who want to establish their residence in Spain. As can be seen in estimate 2, the incidence of the percentage of foreigners in the price of cave houses is higher than in estimate 3 for single-family houses. This suggests that there is a positive relationship between the price of cave housing, the existence of cave house offers and the presence of a higher foreign population.
In addition to the above factors, there are other aspects that have enhanced the value of this type of housing:
-
Firstly, it may be due to the effort made for many years by different public entities that have been able to promote the tourist value of cave houses through different projects and financing initiatives such as the Rural Development Leader programs I, II, and +, the Proder Operational Programs I and II for the Development and Economic Diversification of Rural Areas [99,100], and the Eurocuevas Project, launched by the provincial council of Granada, which has received financial aid from the European Union under the European Regional Development Fund—ERDF—and has had the following results: (a) the creation of an international association of caves [101]; (b) the creation of an interpretation center in the cave houses of Benamaurel [102]; (c) the start of the first phase of creating an inventory of cave houses in the province, which involves a series of provincial plans that improve the urban environment of cave houses [103,104,105]; and (d) the drafting of a manual for intervention in cave houses, with the aim of providing municipal technicians with tools to be able to carry out renovation work or new construction [106]. Moreover, Andalusia is one of the first autonomous communities to regulate the cave house typology through the Law for the Promotion of Territorial Sustainability in Andalusia [107].
-
Secondly, there are construction technicians who are trying to promote the renovation of cave houses by producing documents that outline the necessary process to render them legal and the considerations that must be adopted in order to adapt this type of construction to the national regulations, that is, the Technical Building Code, which regulates the basic requirements [47,108].
-
Thirdly, the promotion and distribution of this type of construction through the design of a cave house with the “ideas competition” promoted by the project “La Herradura” of Huéscar with the participation of the international research group “Aedificatio” from the University of Alicante, the City Council of Huéscar, and the Rural Development Group of the Altiplano de Granada, with the support of Forum UNESCO—University and Heritage [109].
Through the second objective, we propose to identify whether the energy rating is a relevant characteristic of cave houses, and, as such, the second hypothesis—H2—is proposed. Estimate 2 rejects this hypothesis. Regarding energy rating, a very small percentage of the sample provides information about the energy rating; around 8% of cave houses and 11% of single-family houses. The result about energy ratings for the cave house sample suggests that this characteristic is not relevant for the estimation of house prices. These results could be due to the fact that sellers and buyers know the thermal advantages of cave houses, which is why the letter of the energy rating is not taken into consideration in this typology. Therefore, it would be interesting to gather data in different sociocultural and economic contexts on the population of Granada to analyze the perception they have about the energy rating of cave houses and identify whether they are aware of the energy-saving potential of this typology against other houses. In contrast, in single-family houses, values in energy rating premiums are estimated in accordance with the literature, since houses with high—letters A and B—ratings obtain positive premiums of 8.6% and 14.2%, respectively. Houses with a low rating—letter G—receive a negative premium of 7.6%.
Regarding the HVAC systems, the third hypothesis—H3—is proposed. Estimates 2 and 3 (Table 3 and Figure 5) confirm that the availability of air-conditioning is not a determining characteristic at the time of buying a cave house. However, the availability of heating or a fireplace is a determining characteristic. The reason for these results may be because cave houses, as opposed to other typologies such as single-family houses, have a ground enclosure that has high thermal inertia and reduces heat gains in the summer, meaning it would not be necessary for these houses to have air-conditioning. However, the availability of air-conditioning is in fact a determining characteristic in terms of the price of single-family houses (with an economic premium of 5.4%). At the same time, cave houses reduce heat loss in winter due to the conduction of the enclosure (high density of the soil). This is in such a way that those cave houses that have heating have an economic premium that is four times that of single-family houses that are in similar condition (11.3% compared to 47.2%). Similarly, in cave houses, the availability of a chimney is positively valued (13.4%) since, as suggested by many authors [15,24,110], it is one of the characteristics of caves in Granada, while in single-family houses, this is not a determining characteristic.
With regard to neighborhood characteristics, cave houses increase in price three times more than single-family houses if they are located in areas with a higher percentage of foreigners. This price difference between typologies may be due, in the first place, to cave houses not being accepted by the local population for either socioeconomic or cultural reasons [45,110,111]. Secondly, as noted by Mejías del Río [48], at the end of 1990, an economic sector orientated toward foreigners was developed and was dedicated to the renovation and sale of cave houses.
In contrast, in areas where there is a high population of elderly people, the price premium of cave houses is four times lower than that of single-family houses. This difference in premiums may be due to these areas being depopulated, unequipped, and unattractive [112]. Lastly, the percentage of people with university studies has the same impact on cave houses as it does on single-family houses.

5. Conclusions

This study looks to study whether sustainable constructions, such as cave houses, have an economic impact with regard to other construction types in the real estate market in Granada. In addition, it is determined whether the energy rating and air conditioning systems are relevant characteristics in determining the price of cave houses. As far as the authors are aware, there have been no studies that analyze the prices of cave houses.
To carry out the research, a regression model estimated by ordinary least squares is used, using a sample of 61,573 properties offered for sale, both single-family houses and cave houses. Real estate prices have been collected in two time periods between 2017 and 2021 from two Spanish real estate portals.
In conclusion, society is changing its perception of cave houses and is beginning to consider them to be a sustainable construction typology that allows for significant energy savings (between 23% and 35%) compared to other typologies, according to the results obtained by Kumar, et al. [113]. The consequence of this is that currently cave houses are being marketed at higher prices than single-family houses and the energy rating or the availability of air-conditioning are not determining characteristics of the price.
This study, as previously mentioned, has some limitations due to the lack of information from official resources regarding the transaction prices and the characteristics of the houses. The results are obtained from the people offering houses and refer to a specific place (province of Granada) and cannot be extrapolated to other places.
At the same time, the results are encouraging for the government as it can be considered that the economic aid and the projects carried out are having a significant impact on society, given that the energy crisis is having serious repercussions on energy costs.
This study presents many lines for future research. The first is the opportunity to analyze the perception of the population of Granada in terms of the economic and sustainable advantages of cave houses. The second is the opportunity to analyze the costs of renovating cave houses and complying with construction standards. The third is the opportunity to analyze the prices of cave houses within the tourist sector and the sharing economy.

Author Contributions

All authors contributed equally to this work. Conceptualization, M.-F.C.-L. and R.-T.M.-G.; methodology, M.-F.C.-L. and R.-T.M.-G.; software, M.-F.C.-L. and R.-T.M.-G.; formal analysis, M.-F.C.-L. and R.-T.M.-G.; investigation, M.-F.C.-L. and R.-T.M.-G.; data curation, M.-F.C.-L. and R.-T.M.-G.; writing—review and editing, M.-F.C.-L. and R.-T.M.-G.; visualization, M.-F.C.-L. and R.-T.M.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The authors do not have permission to share data.

Acknowledgments

Acknowledgement of the “Research project for innovation in earthen architecture, cave housing and urban development” (AYTOHUESCAR2-20T), financed by the Huescar City Council and its main researcher Antonio Jiménez Delgado.

Conflicts of Interest

The authors declare no conflict of interest.

Note

1
A comarca is a division of territory comprising several municipalities, forming an intermediate level of administrative subdivision between the municipalities and the provinces.

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Figure 1. (a) Cave house in the Baza area. (b) Single-family houses in Guadix town. Source: own elaboration.
Figure 1. (a) Cave house in the Baza area. (b) Single-family houses in Guadix town. Source: own elaboration.
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Figure 2. (a) Map of the Granada province with the delimitation of the comarcas and municipalities (own elaboration); (b) Towns with cave dwellings in Spain, own elaboration based on [12] (p. 3, Figure 2).
Figure 2. (a) Map of the Granada province with the delimitation of the comarcas and municipalities (own elaboration); (b) Towns with cave dwellings in Spain, own elaboration based on [12] (p. 3, Figure 2).
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Figure 3. (a) Spatial distribution of cave houses; (b) Spatial distribution of single-family houses; (c) Bar chart with unit prices according to house typology and comarca; (d) Kernel density map with unit prices of the complete sample of houses (euros/m2); (e) Chart of the energy rating distribution of cave houses by comarca; (f) Chart of the energy rating distribution of single-family houses by comarca.
Figure 3. (a) Spatial distribution of cave houses; (b) Spatial distribution of single-family houses; (c) Bar chart with unit prices according to house typology and comarca; (d) Kernel density map with unit prices of the complete sample of houses (euros/m2); (e) Chart of the energy rating distribution of cave houses by comarca; (f) Chart of the energy rating distribution of single-family houses by comarca.
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Figure 4. Graphs of the estimate 1: (a) Histogram and normal curve of the standardized residual error; (b) Normal P-P plot of regression standardized residual; (c) Scatter plot of the predicted values and standardized errors.
Figure 4. Graphs of the estimate 1: (a) Histogram and normal curve of the standardized residual error; (b) Normal P-P plot of regression standardized residual; (c) Scatter plot of the predicted values and standardized errors.
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Land 11 02185 g005 550
Figure 5. Bar chart with the regression coefficients (in %) of estimate 2 (sample of cave houses) and estimate 3 (sample of single-family houses). Notes: dependent variable Ln_price; signification: *** p < 0.001, ** p < 0.01, * p < 0.05.
Figure 5. Bar chart with the regression coefficients (in %) of estimate 2 (sample of cave houses) and estimate 3 (sample of single-family houses). Notes: dependent variable Ln_price; signification: *** p < 0.001, ** p < 0.01, * p < 0.05.
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Table
Table 1. Variables used by other authors for the determination of single-family house prices.
Table 1. Variables used by other authors for the determination of single-family house prices.
CategoryCharacteristicsReferences
House characteristicsHouse surface area[51,67,68,69,70,71,72,73,74]
Number of bedrooms[70,71,75,76]
Number of bathrooms or toilets[69,70,71,72,76,77]
Availability of air conditioning[51,69,70]
Availability of heating[69,76,77,78,79]
Availability of fireplace[71,72,76,77,80]
Availability of built-in closets[81,82]
Availability of furniture-
Newly built housing[83,84,85]
House typology[71,74,75,86,87]
Parking[70,76,77,80]
Garden[51,67,68]
Swimming pool[51,69,74,77,80,84]
Energy rating[34,38,41,75,86,88]
Location characteristicsLocation within the territory or the city[67,68,74,75,76,77,86]
Neighborhood characteristicsAge of the population[37,89,90]
Number of foreigners[87,89,91]
Level of studies[79,87,92]
Type of house occupancy[71,72,79]
Table
Table 2. Set of variables that make up the study, with their units, description and descriptive statistics.
Table 2. Set of variables that make up the study, with their units, description and descriptive statistics.
CategoryVariableUnitVariable DescriptionContinuous VariablesDummies Variables
MeanSDMin.Max.Coding.Freq.%
Ln_PricenumericalDependent variable. The natural log of the property price offered by the seller (in EUR).12.1050.6689.214.8
House characteristics (A)Area_m2numericalBuilt area of the house (sqm), gross square meters of the house.214.854111.88420.01746.0
BedroomsnumericalNumber of bedrooms in the house.3.8061.3751.029.0
BathroomsnumericalNumber of bathrooms.2.3481.0230.011.0
ToiletsnumericalNumber of toilets.0.1930.4560.010.0
Air_conddummyAvailability of air conditioning (=1). (1) With21,00534.1
HeatingdummyAvailability of heating (=1). (1) With24,17039.3
FireplacedummyAvailability of fireplace (=1). (1) With20803.4
ClosetdummyAvailability of built-in closets (=1). (1) With54588.9
Furnished_housedummyAvailability of furniture (=1). (1) With37686.1
New_constructiondummyNewly built housing that can be: a project, under construction, or less than 3 years old (=1). (1) With3430.6
Cave_housedummyIndicates whether the property has this typology (=1). (1) With4400.7
ParkingdummyAvailability of garage slot (=1). (1) With37,61861.1
GardendummyAvailability of garden (=1). (1) With621910.1
PooldummyAvailability of swimming pool (=1). (1) With22,48236.5
Letter_AdummyIndicates if the dwelling has an energy rating: letters A, B, C, D, E, F or G, or has no energy rating label. (1) With2850.5
Letter_Bdummy (1) With1660.3
Letter_Cdummy (1) With2790.5
Letter_Ddummy (1) With10211.7
Letter_Edummy (1) With29234.7
Letter_Fdummy (1) With6841.1
Letter_Gdummy (1) With10661.7
Not_EPCdummy (1) With55,14989.6
Location characteristics (B)AlhamadummyIdentifier of the comarca: Alhama, Alpujarra Granadina, Baza, Costa Granadina, Guadix, Huéscar, Loja, Los Montes, Valle de Lecrín and Vega de Granada. (1) With5200.8
Alpujarra_Granadinadummy (1) With8861.4
Bazadummy (1) With6511.1
Costa_Granadinadummy (1) With51378.3
Guadixdummy (1) With6031.0
Huescardummy (1) With2300.4
Lojadummy (1) With10731.7
Los_Montesdummy (1) With4620.7
Valle_Lecrindummy (1) With22403.6
Vega_Granada (Ref.)dummy (1) With49,77180.8
Neighborhood characteristics (C)Dependency_rationumericalDependency ratio (sum of the population aged >64 and <16/population aged 16–64).0.4740.1330.01.8
Elderly_indexnumericalAging index (population aged >64/population aged 0–15).0.8360.8630.011.5
Foreigners_pctnumericalPercentage of foreign population.6.1527.1830.052.6
No_studies_pctnumericalPercentage of population without education.8.4496.6200.044.8
University_pctnumericalPercentage of the population with university studies.18.78010.8220.058.5
Secondary_pctnumericalPercentage of vacant dwellings and secondary.12.53714.2730.081.9
Vacant_pctnumerical18.0589.7110.075.7
Table
Table 3. OLS model results for the different estimates depending on the selected sample (Estimate 1: full sample; Estimate 2: cave houses and Estimate 3: single-family houses).
Table 3. OLS model results for the different estimates depending on the selected sample (Estimate 1: full sample; Estimate 2: cave houses and Estimate 3: single-family houses).
CategoryVariableEstimate 1:
Full Sample		Estimate 2:
Cave Houses		Estimate 3:
Single-Family Houses
Intercept10.536 *** (0.011)10.633 *** (0.304)10.539 *** (0.011)
House characteristics (A)Area_m20.002 *** (0.000)0.001 *** (0.000)0.002 *** (0.000)
Bedrooms0.026 *** (0.001)0.038 ** (0.014)0.025 *** (0.001)
Bathrooms0.157 *** (0.002)0.238 *** (0.025)0.154 *** (0.002)
Toilets0.108 *** (0.004)0.092 (0.065)0.108 *** (0.004)
Air_cond0.053 *** (0.003)–0.012 (0.067)0.054 *** (0.003)
Heating0.114 *** (0.004)0.472 *** (0.079)0.113 *** (0.004)
Fireplace–0.004 (0.009)0.134 * (0.067)–0.006 (0.009)
Closet0.067 *** (0.006)0.139 (0.084)0.066 *** (0.006)
Furnished_house0.071 *** (0.006)0.299 *** (0.086)0.068 *** (0.006)
New_construction0.069 *** (0.020)─0.069 *** (0.020)
Cave_house0.053 ** (0.019)──
Parking0.081 *** (0.003)0.157 * (0.062)0.083 *** (0.003)
Garden0.097 *** (0.006)0.101 (0.086)0.098 *** (0.006)
Pool0.198 *** (0.004)0.149 (0.101)0.200 *** (0.004)
Letter_A0.090 *** (0.023)0.311 (0.275)0.086 *** (0.023)
Letter_B0.139 *** (0.030)0.179 (0.259)0.142 *** (0.030)
Letter_C0.006 (0.024)0.379 (0.364)0.005 (0.023)
Letter_D0.052 *** (0.014)─0.052 *** (0.014)
Letter_ERef.Ref.Ref.
Letter_F–0.011 (0.016)–0.096 (0.263)–0.014 (0.016)
Letter_G–0.073 *** (0.013)0.359 (0.233)–0.076 *** (0.013)
Not_EPC0.047 *** (0.007)0.085 (0.147)0.047 *** (0.007)
Location characteristics (B)Alhama–0.315 *** (0.017)─–0.316 *** (0.017)
Alpujarra_Granadina–0.337 *** (0.014)─–0.336 *** (0.014)
Baza–0.472 *** (0.016)–0.646 *** (0.167)–0.444 *** (0.017)
Costa_Granadina0.211 *** (0.007)─0.213 *** (0.007)
Guadix–0.262 *** (0.016)–0.464 ** (0.164)–0.233 *** (0.017)
Huescar–0.545 *** (0.026)–0.779 *** (0.183)–0.484 *** (0.028)
Loja–0.179 *** (0.013)─–0.179 *** (0.013)
Los_Montes–0.394 *** (0.019)─–0.383 *** (0.019)
Valle_Lecrin–0.174 *** (0.009)─–0.174 *** (0.009)
Vega_GranadaRef.Ref.Ref.
Neighborhood characteristics (C)Dependency_ratio0.146 *** (0.013)0.075 (0.243)0.151 *** (0.013)
Elderly_index0.041 *** (0.002)–0.111 ** (0.038)0.042 *** (0.002)
Foreigners_pct0.007 *** (0.000)0.020 *** (0.005)0.006 *** (0.000)
No_studies_pct–0.003 *** (0.000)0.001 (0.006)–0.003 *** (0.000)
University_pct0.018 *** (0.000)0.014 * (0.006)0.018 *** (0.000)
Secondary_pct–0.001 *** (0.000)–0.001 (0.003)–0.001 *** (0.000)
Vacant_pct–0.0004 * (0.0002)0.0002 (0.0027)–0.0004 * (0.0002)
N61,57341361,133
R20.6860.8220.685
adj. R20.6850.8090.685
Std. Error0.3750.4600.373
F (sig.)3625.41 (0.000)63.23 (0.000)3586.14 (0.000)
Durbin-Watson1.8061.8591.803
Notes: dependent variable Ln_price; signification: *** p < 0.001, ** p < 0.01, * p < 0.05; Standard errors in parentheses.
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Cespedes-Lopez, M.-F.; Mora-Garcia, R.-T. Are Cave Houses a Sustainable Real Estate Alternative? Land 2022, 11, 2185. https://doi.org/10.3390/land11122185

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Cespedes-Lopez M-F, Mora-Garcia R-T. Are Cave Houses a Sustainable Real Estate Alternative? Land. 2022; 11(12):2185. https://doi.org/10.3390/land11122185

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Cespedes-Lopez, Maria-Francisca, and Raul-Tomas Mora-Garcia. 2022. "Are Cave Houses a Sustainable Real Estate Alternative?" Land 11, no. 12: 2185. https://doi.org/10.3390/land11122185

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