# RECORD 0e8221fad5dffd447b52c9be164621765eb67679
# TITLE: From peaks to prices: the economic impact of natural amenities on alpine real estate values
# YEAR: 2025.0  VENUE: Journal of European Real Estate Research  DOI: 10.1108/jerer-02-2025-0033
# AUTHORS: Carolina Bonardi Pellizzari; Luisa Eusse-Villa; C. Franceschinis; T. Tempesta; Mara Thiene; D. Vecchiato
# PAGES: 20  CANDIDATE TABLE PAGES (1-based): [10]
# ABSTRACT: 
 
 This study aims to analyse the effects of environmental and climate (dis)amenities on house prices in the Italian Alps.
 
 
 
 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.
 
 
 
 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.
 
 
 
 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.



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From peaks to prices: the economic 
impact of natural amenities 
on alpine real estate values
Carolina Bonardi Pellizzari, Luisa Eusse-Villa, 
Cristiano Franceschinis, Tiziano Tempesta, Mara Thiene and 
Daniel Vecchiato
Department of Land, Environment, Agriculture and Forestry (TESAF), 
Universita degli Studi di Padova, Legnaro, Italy
Abstract
Purpose – This study aims to analyse the effects of environmental and climate (dis)amenities on house prices in 
the Italian Alps.
Design/methodology/approach – 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.
Findings – 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.
Originality/value – 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.
Keywords Landscape, Hedonic pricing, Tourism, Spatial models, Ecosystem services, Real estate values
Paper type Research article
1. Introduction
Mountains are key in providing ecosystem services (ES), such as clean water, fresh air, 
biodiversity, wood provision and avalanche prevention through their forests. Mountains are 
also popular for recreation and tourism, offering scenic landscapes, heritage and opportunities 
for sports (Martin-Lopez et al., 2019). The growing appreciation for outdoor spaces, 
particularly after the coronavirus disease 2019 (COVID-19) pandemic, has led to increased 
visits, especially in areas near urban centres with improved accessibility (da Schio et al., 2021; 
Grima et al., 2020; Sikorska et al., 2023). Several ES are experiencing this rising demand, and 
their value is not easily determined due to the lack of existing markets.
One effective method to estimate the (use) value of ES in monetary terms is by examining 
the prices of goods that exhibit a degree of complementarity with them. Studies using hedonic
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© Carolina Bonardi Pellizzari, Luisa Eusse-Villa, Cristiano Franceschinis, Tiziano Tempesta, Mara 
Thiene and Daniel Vecchiato. Published by Emerald Publishing Limited. This article is published under 
the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and 
create derivative works of this article (for both commercial and non-commercial purposes), subject to full 
attribution to the original publication and authors. The full terms of this licence may be seen at Link to the 
terms of the CC BY 4.0 licence.
Funding: This work was supported by the University of Padova in the framework of the project 
“ENVALUE: The role of the ENvironment and energy efficiency on house and farmland VALUEs” 
(Grant: BIRD 225784).
The current issue and full text archive of this journal is available on Emerald Insight at: 
https://www.emerald.com/insight/1753-9269.htm
Received 10 March 2025 
Revised 16 May 2025 
Accepted 25 June 2025
Journal of European Real Estate Research 
Vol. 18 No. 2, 2025
pp. 230-249
Emerald Publishing Limited
e-ISSN: 1753-9277
p-ISSN: 1753-9269
DOI 10.1108/JERER-02-2025-0033
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3.2 Spatial effects on hedonic models
Traditional hedonic models often overlook spatial autocorrelation, where nearby properties 
influence a dwelling’s value. We incorporated spatial analysis techniques into our hedonic 
models to address this issue. Our first result from this analysis concerns the quantification of 
spatial autocorrelation using the Global Moran’s Index (MI) to quantify its effect. The results
Table 2. Hedonic model results
Variables
OLS
SAR
Intrinsic variables 
Property size

0.003***

0.003***
(0.001)
(0.001)
EPC (Ref 5 G)
ABCD
0.324**
0.337***
(0.0764)
(0.074)
E
0.226**
0.233***
(0.0706)
(0.069)
F
0.202***
0.205***
(0.058)
(0.057)
Building age

0.011***

0.012***
(0.001)
(0.001)
Building age (^2)
0.000***
0.000***
0.000
0.000
Dwelling type (Ref 5 Apartment) 
House

0.104

0.087
(0.107)
(0.104)
Portion of the house

0.210***

0.200***
(0.052)
(0.051)
Bathrooms
0.165**
0.157**
(0.055)
(0.054)
Mediation (Ref 5 No mediation)
0.121**
0.119**
(0.044)
(0.042)
Extrinsic variables
Number of hotels
0.046*
0.049*
(0.020)
(0.019)
Forest cover (250 m radius)

0.004***

0.003**
(0.001)
(0.001)
Log of distance to the ski lift

0.053

0.027
(0.029)
(0.029)
Log of distance to primary road
0.059***
0.053***
(0.016)
(0.016)
Winter temperature

0.136***

0.088**
(0.031)
(0.032)
Winter temperature (^2)

0.032**

0.023*
(0.011)
(0.011)
Spatial lag (ρ)
0.39**
(0.115)
Constant
7.730***
4.754***
(0.264)
(0.904)
R 2 (Pseudo R 2 for SAR)
0.606
0.615
Adjusted R 2
0.592
Log-Likelihood

256.117

251.826
AIC
548.235
541.65
Note(s): Dependent variable 5 logged dwelling prices; EPC 5 Energy Performance Certificate; standard errors
in parenthesis. *p < 0.05; **p < 0.01; ***p < 0.001
Source(s): Authors’ own work
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confirm spatial clustering as the Global MI shows a positive and statistically significant 
coefficient of 0.115 (p-value <0.05 and z-value 5 15.201). Supplementary Figure 2 presents 
the resulting scatterplot. The plot is divided into quadrants to analyse spatial autocorrelation, 
identifying four types: high-high, low-low, high-low and low-high. A Local Indicators of 
Spatial Association (LISA) test further visualizes this, highlighting clusters shown in Figure 4. 
The map reveals hot and cold spots in the real estate market, with high-price clusters (in red) in 
Falcade, Agordo and areas near Cortina d’Ampezzo, while cold spots (in blue) are mainly in 
the centre and south.
Moran’s I test offers a global measure of spatial autocorrelation. However, it offers limited 
guidance concerning the most suitable spatial model for our data. In contrast, the Lagrange 
Multiplier test (LM) helps identify the suitable spatial model. The LM test results in 
Supplementary Table 3 confirm that the SAR model fits the spatial pattern of the data. The LM 
tests produced significant results for both spatial and robust lag terms, with p-values of less 
than 0.01. This suggests that neighbouring dwelling prices significantly influence each other. 
Based on these results, we selected the SAR specification for the model, as shown in column 3 
of Table 2.
The spatial lag coefficient is highly significant, highlighting the importance of considering 
spatial dependencies when analysing property values. Specifically, the spatial lag coefficient is 
0.39 (p < 0.01). This result confirms the presence of spatial dependence and the need to use 
spatial specifications in hedonic modelling.
In the SAR model, intrinsic factors such as property size, EPC ratings, building age, and the 
number of bathrooms consistently significantly affected dwelling prices. However, the SAR 
model revealed spatial dependencies that the OLS model might have overlooked. For instance, 
extrinsic variables like the winter temperature and distance to primary road showed different 
results, indicating spatial variations in their impact on property values. The distance to the nearest 
ski lift showed a negative effect in the OLS, but in the SAR, this variable is not statistically 
significant. Therefore, due to their ability to account for spatial autocorrelation, spatial models 
tend to moderate the effect of other variables that have a greater impact on OLS models.
The differences in the coefficients emphasize the importance of considering spatial 
dependencies. In the OLS model, the winter temperature coefficient was substantially higher 
(
0.136 and 
0.032 for the quadratic term) compared to the SAR model (
0.088 and 
0.023 
for the quadratic term). This suggests that higher average winter temperatures are associated 
with lower property prices, but this effect is less pronounced when spatial autocorrelation is 
considered.
This reduction in coefficients in the SAR model occurs because the OLS model overstates 
the impact of variables by ignoring neighbouring property values. The SAR model better
Figure 3. Estimated effect of winter temperature on property price in the study area. The plot illustrates the 
relationship between the property values (in €/m2) and the winter temperatures (in 8C). Source: Authors’ own 
work. The analysis was conducted using R version 4.2.3 (R Core Team, 2023)
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