1# RECORD 0e8221fad5dffd447b52c9be164621765eb676792# TITLE: From peaks to prices: the economic impact of natural amenities on alpine real estate values3# YEAR: 2025.0 VENUE: Journal of European Real Estate Research DOI: 10.1108/jerer-02-2025-00334# AUTHORS: Carolina Bonardi Pellizzari; Luisa Eusse-Villa; C. Franceschinis; T. Tempesta; Mara Thiene; D. Vecchiato5# PAGES: 20 CANDIDATE TABLE PAGES (1-based): [10]6# ABSTRACT: 7 8 This study aims to analyse the effects of environmental and climate (dis)amenities on house prices in the Italian Alps.9 10 11 12 Drawing on a dataset containing 447 sales contracts, this research adopted two modelling approaches: the classical hedonic model, estimated via ordinary least squares and a spatial model. Combining hedonic models with spatial models offers insights into studying property prices in alpine areas by capturing spatial dependencies of neighbouring areas, thus avoiding biased estimates and improving model accuracy.13 14 15 16 We show that higher winter temperatures significantly decrease property prices, reflecting preferences for higher altitudes with reliable winter sports conditions. Increased forest cover also negatively impacts prices, suggesting a preference for traditional alpine landscapes with panoramic views. Energy-efficient dwellings, however, command higher prices, indicating their value in the market.17 18 19 20 We contribute to the literature by (1) exploring the relationship between house prices and natural amenities in the European Alps; (2) using transaction data instead of property listings in Italy to provide more accurate estimates and (3) accounting for spatial autocorrelation to enhance the robustness of our analysis. Beyond addressing those gaps, our findings provide insights for land use planning and support the development of diversified and sustainable tourism strategies.21222324===== PAGE 1 (score 16, numeric-paren 0, table-words 0, var-words 16) =====2526From peaks to prices: the economic 27impact of natural amenities 28on alpine real estate values29Carolina Bonardi Pellizzari, Luisa Eusse-Villa, 30Cristiano Franceschinis, Tiziano Tempesta, Mara Thiene and 31Daniel Vecchiato32Department of Land, Environment, Agriculture and Forestry (TESAF), 33Universita degli Studi di Padova, Legnaro, Italy34Abstract35Purpose – This study aims to analyse the effects of environmental and climate (dis)amenities on house prices in 36the Italian Alps.37Design/methodology/approach – Drawing on a dataset containing 447 sales contracts, this research adopted 38two modelling approaches: the classical hedonic model, estimated via ordinary least squares and a spatial model. 39Combining hedonic models with spatial models offers insights into studying property prices in alpine areas by 40capturing spatial dependencies of neighbouring areas, thus avoiding biased estimates and improving model 41accuracy.42Findings – We show that higher winter temperatures significantly decrease property prices, reflecting 43preferences for higher altitudes with reliable winter sports conditions. Increased forest cover also negatively 44impacts prices, suggesting a preference for traditional alpine landscapes with panoramic views. Energy-efficient 45dwellings, however, command higher prices, indicating their value in the market.46Originality/value – We contribute to the literature by (1) exploring the relationship between house prices and 47natural amenities in the European Alps; (2) using transaction data instead of property listings in Italy to provide 48more accurate estimates and (3) accounting for spatial autocorrelation to enhance the robustness of our analysis. 49Beyond addressing those gaps, our findings provide insights for land use planning and support the development 50of diversified and sustainable tourism strategies.51Keywords Landscape, Hedonic pricing, Tourism, Spatial models, Ecosystem services, Real estate values52Paper type Research article531. Introduction54Mountains are key in providing ecosystem services (ES), such as clean water, fresh air, 55biodiversity, wood provision and avalanche prevention through their forests. Mountains are 56also popular for recreation and tourism, offering scenic landscapes, heritage and opportunities 57for sports (Martin-Lopez et al., 2019). The growing appreciation for outdoor spaces, 58particularly after the coronavirus disease 2019 (COVID-19) pandemic, has led to increased 59visits, especially in areas near urban centres with improved accessibility (da Schio et al., 2021; 60Grima et al., 2020; Sikorska et al., 2023). Several ES are experiencing this rising demand, and 61their value is not easily determined due to the lack of existing markets.62One effective method to estimate the (use) value of ES in monetary terms is by examining 63the prices of goods that exhibit a degree of complementarity with them. Studies using hedonic64JERER6518,26623067© Carolina Bonardi Pellizzari, Luisa Eusse-Villa, Cristiano Franceschinis, Tiziano Tempesta, Mara 68Thiene and Daniel Vecchiato. Published by Emerald Publishing Limited. This article is published under 69the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and 70create derivative works of this article (for both commercial and non-commercial purposes), subject to full 71attribution to the original publication and authors. The full terms of this licence may be seen at Link to the 72terms of the CC BY 4.0 licence.73Funding: This work was supported by the University of Padova in the framework of the project 74“ENVALUE: The role of the ENvironment and energy efficiency on house and farmland VALUEs” 75(Grant: BIRD 225784).76The current issue and full text archive of this journal is available on Emerald Insight at: 77https://www.emerald.com/insight/1753-9269.htm78Received 10 March 2025 79Revised 16 May 2025 80Accepted 25 June 202581Journal of European Real Estate Research 82Vol. 18 No. 2, 202583pp. 230-24984Emerald Publishing Limited85e-ISSN: 1753-927786p-ISSN: 1753-926987DOI 10.1108/JERER-02-2025-003388Downloaded from http://www.emerald.com/jerer/article-pdf/18/2/230/10177814/jerer-02-2025-0033en.pdf by guest on 16 October 2025899091===== PAGE 10 (score 85, numeric-paren 64, table-words 2, var-words 9) =====92933.2 Spatial effects on hedonic models94Traditional hedonic models often overlook spatial autocorrelation, where nearby properties 95influence a dwelling’s value. We incorporated spatial analysis techniques into our hedonic 96models to address this issue. Our first result from this analysis concerns the quantification of 97spatial autocorrelation using the Global Moran’s Index (MI) to quantify its effect. The results98Table 2. Hedonic model results99Variables100OLS101SAR102Intrinsic variables 103Property size1040.003***1050.003***106(0.001)107(0.001)108EPC (Ref 5 G)109ABCD1100.324**1110.337***112(0.0764)113(0.074)114E1150.226**1160.233***117(0.0706)118(0.069)119F1200.202***1210.205***122(0.058)123(0.057)124Building age1250.011***1260.012***127(0.001)128(0.001)129Building age (^2)1300.000***1310.000***1320.0001330.000134Dwelling type (Ref 5 Apartment) 135House1360.1041370.087138(0.107)139(0.104)140Portion of the house1410.210***1420.200***143(0.052)144(0.051)145Bathrooms1460.165**1470.157**148(0.055)149(0.054)150Mediation (Ref 5 No mediation)1510.121**1520.119**153(0.044)154(0.042)155Extrinsic variables156Number of hotels1570.046*1580.049*159(0.020)160(0.019)161Forest cover (250 m radius)1620.004***1630.003**164(0.001)165(0.001)166Log of distance to the ski lift1670.0531680.027169(0.029)170(0.029)171Log of distance to primary road1720.059***1730.053***174(0.016)175(0.016)176Winter temperature1770.136***1780.088**179(0.031)180(0.032)181Winter temperature (^2)1820.032**1830.023*184(0.011)185(0.011)186Spatial lag (ρ)1870.39**188(0.115)189Constant1907.730***1914.754***192(0.264)193(0.904)194R 2 (Pseudo R 2 for SAR)1950.6061960.615197Adjusted R 21980.592199Log-Likelihood200256.117201251.826202AIC203548.235204541.65205Note(s): Dependent variable 5 logged dwelling prices; EPC 5 Energy Performance Certificate; standard errors206in parenthesis. *p < 0.05; **p < 0.01; ***p < 0.001207Source(s): Authors’ own work208Journal of 209European Real 210Estate Research211239212Downloaded from http://www.emerald.com/jerer/article-pdf/18/2/230/10177814/jerer-02-2025-0033en.pdf by guest on 16 October 2025213214215===== PAGE 11 (score 23, numeric-paren 0, table-words 7, var-words 8) =====216217confirm spatial clustering as the Global MI shows a positive and statistically significant 218coefficient of 0.115 (p-value <0.05 and z-value 5 15.201). Supplementary Figure 2 presents 219the resulting scatterplot. The plot is divided into quadrants to analyse spatial autocorrelation, 220identifying four types: high-high, low-low, high-low and low-high. A Local Indicators of 221Spatial Association (LISA) test further visualizes this, highlighting clusters shown in Figure 4. 222The map reveals hot and cold spots in the real estate market, with high-price clusters (in red) in 223Falcade, Agordo and areas near Cortina d’Ampezzo, while cold spots (in blue) are mainly in 224the centre and south.225Moran’s I test offers a global measure of spatial autocorrelation. However, it offers limited 226guidance concerning the most suitable spatial model for our data. In contrast, the Lagrange 227Multiplier test (LM) helps identify the suitable spatial model. The LM test results in 228Supplementary Table 3 confirm that the SAR model fits the spatial pattern of the data. The LM 229tests produced significant results for both spatial and robust lag terms, with p-values of less 230than 0.01. This suggests that neighbouring dwelling prices significantly influence each other. 231Based on these results, we selected the SAR specification for the model, as shown in column 3 232of Table 2.233The spatial lag coefficient is highly significant, highlighting the importance of considering 234spatial dependencies when analysing property values. Specifically, the spatial lag coefficient is 2350.39 (p < 0.01). This result confirms the presence of spatial dependence and the need to use 236spatial specifications in hedonic modelling.237In the SAR model, intrinsic factors such as property size, EPC ratings, building age, and the 238number of bathrooms consistently significantly affected dwelling prices. However, the SAR 239model revealed spatial dependencies that the OLS model might have overlooked. For instance, 240extrinsic variables like the winter temperature and distance to primary road showed different 241results, indicating spatial variations in their impact on property values. The distance to the nearest 242ski lift showed a negative effect in the OLS, but in the SAR, this variable is not statistically 243significant. Therefore, due to their ability to account for spatial autocorrelation, spatial models 244tend to moderate the effect of other variables that have a greater impact on OLS models.245The differences in the coefficients emphasize the importance of considering spatial 246dependencies. In the OLS model, the winter temperature coefficient was substantially higher 247(0.136 and 0.032 for the quadratic term) compared to the SAR model (0.088 and 0.023 248for the quadratic term). This suggests that higher average winter temperatures are associated 249with lower property prices, but this effect is less pronounced when spatial autocorrelation is 250considered.251This reduction in coefficients in the SAR model occurs because the OLS model overstates 252the impact of variables by ignoring neighbouring property values. The SAR model better253Figure 3. Estimated effect of winter temperature on property price in the study area. The plot illustrates the 254relationship between the property values (in €/m2) and the winter temperatures (in 8C). Source: Authors’ own 255work. The analysis was conducted using R version 4.2.3 (R Core Team, 2023)256JERER25718,2258240259Downloaded from http://www.emerald.com/jerer/article-pdf/18/2/230/10177814/jerer-02-2025-0033en.pdf by guest on 16 October 2025260