scholarly journals The Uneven Price Impact of Energy Efficiency Ratings on Housing Segments and Implications for Public Policy and Private Markets

2019 ◽  
Vol 11 (2) ◽  
pp. 372 ◽  
Author(s):  
Carlos Marmolejo-Duarte ◽  
Ai Chen
Keyword(s):  
Low Income ◽  
State Of The Art ◽  
Housing Prices ◽  
Energy Performance ◽  
Price Impact ◽  
Hedonic Model ◽  
Entire Sample ◽  
Energy Retrofit ◽  
Basic Features ◽  
The Impact

In the literature, there is extensive, although in some cases inconclusive, evidence on the impact of Energy Performance Certificates (EPC) on housing prices. Nonetheless, the question of whether such an impact is homogenous across residential segments remains highly unexplored. This paper addresses this latter issue utilizing multifamily listing data in metropolitan Barcelona. In doing so, first the entire sample is analyzed using a hedonic model. Second, the sample is split on the basis of a multivariate segmentation. Finally, separated hedonic models are specified again. The results suggest that in general, there is a modest impact of EPC ratings on listing prices, nonetheless it is not homogeneous across housing segments: (1) for the most modern apartments, with state-of-the-art features and active environmental comfort, energy ratings seem to play a null role in the formation of prices; (2) conversely, for the cheapest apartments, apartments boasting the most basic features, and apartments located in low-income areas, the “brown discount” is enormously significant, potentially depreciating the equity of those who have the least resources to carry out an energy retrofit. These results have implications for the assessment of the EPBD and its Spanish transposition, since a very well-intentioned environmental policy could have potentially harmful social repercussions in the absence of corrective measures.

Research on the Impacts of Real Estate on Economic Growth: A Theoretical Model-Based Analysis

2018 ◽  
Vol 06 (04) ◽  
pp. 1850025
Author(s):  
Xiaoxi ZHANG ◽  
Lu GUO
Keyword(s):  
Economic Growth ◽  
Real Estate ◽  
Low Income ◽  
Housing Prices ◽  
Well Being ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Pillar Industry ◽  
The Impact

As the pillar industry of China’s economy, the real estate sector has a significant impact on macroeconomic growth. We assume that the first stage of economic actors’ working lives is a low-income one, while their second stage is a high-income one. Then, relying on an Overlapping-Generations Model, we analyze how, via real estate, the behaviors of different income groups affect the macroeconomy. The results show that when the supply of real estate market fluctuates then this has an impact on economic growth, but the extent of the impact depends on the relationship between the real estate and the consumer markets. We also find that when economic actors more greatly prefer their current situations of well-being, no matter whether there takes place or not a new increase in real estate stocks, a negative correlation will exist in the relation between real estate stocks and their prices. Lastly, we come to the conclusion that increases in property taxes can effectively reduce housing prices, but the impact of transaction taxes on housing prices can still not be determined.

scholarly journals Housing Prices and the Skills Composition of Neighborhoods

2021 ◽  
Vol 4 ◽  
Author(s):  
Shahad Althobaiti ◽  
Saud Alghumayjan ◽  
Morgan R. Frank ◽  
Esteban Moro ◽  
Ahmad Alabdulkareem ◽  
...  
Keyword(s):  
San Francisco ◽  
Low Income ◽  
Cognitive Skills ◽  
Service Sector ◽  
Housing Prices ◽  
Housing Price ◽  
The United States ◽  
Skilled Workers ◽  
Cost Of Living ◽  
The Impact

In the United States (US), low-income workers are being pushed away from city centers where the cost of living is high. The effects of such changes on labor mobility and housing price have been explored in the literature. However, few studies have focused on the occupations and specific skills that identify the most susceptible workers. For example, it has become increasingly challenging to fill the service sector jobs in the San Francisco (SF) Bay Area because appropriately skilled workers cannot afford the growing cost of living within commuting distance. With this example in mind, how does a neighborhood's skill composition change as a result of higher housing prices? Are there certain skill sets that are being pushed to the geographical periphery of a city despite their essentialness to the city's economy? Our study focuses on the impact of housing prices with a granular view of skills compositions to answer the following question: Has the density of cognitive skill workers been increasing in a gentrified area? We hypothesize that, over time, low-skilled workers are pushed away from downtown or areas where high-skill establishments thrive. Our preliminary results show that high-level cognitive skills are getting closer to the city center indicating adaptation to the increase of median housing prices as opposed to low-level physical skills that got further away. We examined tracts that the literature indicates as gentrified areas and found a pattern in which there is a temporal increase in median housing prices and the number of business establishments coupled with an increase in the percentage of skilled cognitive workers.

scholarly journals Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network

2020 ◽  
Vol 10 (14) ◽  
pp. 4872
Author(s):  
Yang Li ◽  
Zibin Zheng ◽  
Hong-Ning Dai
Keyword(s):  
State Of The Art ◽  
Large Fluctuation ◽  
Prediction Problem ◽  
Time Dependency ◽  
Denoising Autoencoder ◽  
Price Fluctuation ◽  
The Media ◽  
Lstm Network ◽  
Basic Features ◽  
The Impact

Bitcoin has attracted extensive attention from investors, researchers, regulators, and the media. A well-known and unusual feature is that Bitcoin’s price often fluctuates significantly, which has however received less attention. In this paper, we investigate the Bitcoin price fluctuation prediction problem, which can be described as whether Bitcoin price keeps or reversals after a large fluctuation. In this paper, three kinds of features are presented for the price fluctuation prediction, including basic features, traditional technical trading indicators, and features generated by a Denoising autoencoder. We evaluate these features using an Attentive LSTM network and an Embedding Network (ALEN). In particular, an attentive LSTM network can capture the time dependency representation of Bitcoin price and an embedding network can capture the hidden representations from related cryptocurrencies. Experimental results demonstrate that ALEN achieves superior state-of-the-art performance among all baselines. Furthermore, we investigate the impact of parameters on the Bitcoin price fluctuation prediction problem, which can be further used in a real trading environment by investors.

scholarly journals Energy Performance Certificates and Its Capitalization in Housing Values in Sweden

2019 ◽  
Vol 11 (21) ◽  
pp. 6101 ◽  
Author(s):  
Mats Wilhelmsson
Keyword(s):  
Propensity Score ◽  
House Prices ◽  
Housing Prices ◽  
Housing Price ◽  
Energy Performance ◽  
Control Group ◽  
Propensity Score Method ◽  
Propensity Score Methods ◽  
Price Distribution ◽  
The Impact

The impact on energy performance certificates on housing prices has been investigated extensively in recent years. However, the results of these investigations are mixed. We add to the literature by more specifically controlling for potential biases, by employing a combination of alternative approaches to estimate the causal relationship between house prices and energy performance certificates. We use a traditional hedonic modeling approach, but we additionally employ propensity score methods to be able to compare treated houses with a control group. We also investigate the impact of the outliers, spatial dependency, and parameter heterogeneity of our estimates. Moreover, we use the quantile regression technique to test the hypothesis that the capitalization effect varies across the price distribution. Our results, analyzing more than 100,000 observations, indicate there is an upward bias if one is not controlling for outlier and selection bias. Regardless of the propensity score method approach, the results are lower than a model (around 3 percent capitalization, compared to 6 percent). However, our results do not support that the impact of energy performance certificates varies in the price distribution. Consequently, the certificates are not differently capitalized in the high-end housing price segment. Finally, our results support the hypothesis that the energy performance certificate should be more capitalized into house prices in the northern and colder parts of Sweden than in the southern regions.

scholarly journals Effects of New Grocery Store Development on Inner-City Neighborhood Residential Prices

2017 ◽  
Vol 46 (1) ◽  
pp. 87-102 ◽  
Author(s):  
Belkis Cerrato Caceres ◽  
Jacqueline Geoghegan
Keyword(s):  
Inner City ◽  
Housing Prices ◽  
Grocery Store ◽  
Hedonic Model ◽  
Grocery Stores ◽  
Housing Values ◽  
Residential Housing ◽  
Difference In Differences ◽  
Temporal And Spatial ◽  
The Impact

A difference-in-differences approach is used to measure the impact of new inner-city grocery store developments on residential housing values in Worcester, Massachusetts. Using geocoded housing sales from 1988–2011, we develop a hedonic model, exploiting temporal and spatial discontinuities, to identify the effect of 12 new grocery stores on neighborhood housing prices. Results suggest these new stores were associated with an increase in sale prices of nearby homes, and these results could help inform current policies related to urban food deserts, in that new grocery stores have the potential to improve neighborhood wealth as well as health.

scholarly journals Impacts of shopping malls on apartment prices: the case of Stockholm

2020 ◽  
Vol 5 ◽  
pp. 29-48
Author(s):  
Mats Wilhelmsson ◽  
Runfeng Long
Keyword(s):  
Empirical Study ◽  
Inverse Relationship ◽  
Housing Prices ◽  
Shopping Mall ◽  
Hedonic Model ◽  
Price Premium ◽  
Shopping Malls ◽  
The Impact ◽  
The Relationship ◽  
Over Time

Shopping malls, as an important type of commercial facilities, are growing dramatically. They have gradually become one of the most dominant factors that can influence people's daily life as well as a city's economic development. People's willingness to pay for dwellings is also primarily associated with the surrounding commercial layout. Hence, it is of interest to find out more from a quantitative perspective on the relationship between shopping malls and housing prices. This study aims to analyze how the prices of condominiums will be affected by the proximity of shopping malls. Two aspects are considered and examined in the empirical study, namely a proximity to the shopping mall, and the number of shopping malls within 3 kilometers radius. We try to examine if there is any price premium for those apartments near the shopping mall or with more shopping malls in the neighborhood. In this empirical study, 36 shopping malls in different locations in the county of Stockholm, Sweden, is utilized. The sample of transactions consists of 336,914 apartments. By using regression analysis, based on the traditional hedonic model, the results show that there is an inverse relationship between the apartment prices and its distance from the shopping mall while the number of shopping malls is positively correlated with apartment prices. However, the impact has declined over time.

An empirical examination of the effects of land tenure on housing values in Kampala, Uganda

2015 ◽  
Vol 8 (3) ◽  
pp. 359-374 ◽  
Author(s):  
Richard Irumba
Keyword(s):  
Land Tenure ◽  
Housing Prices ◽  
Hedonic Model ◽  
Housing Values ◽  
Cross Sectional ◽  
Empirical Examination ◽  
Content Type ◽  
Transaction Prices ◽  
Lack Of Information ◽  
The Impact

Purpose – The purpose of this paper is to investigate the impact of land tenure on housing values in metropolitan Kampala. Design/methodology/approach – A hedonic model is used to test the relationship between housing prices, land tenure and housing attributes using a cross-sectional dataset of transaction prices for 590 newly built houses sold in 2011. Findings – Public leaseholds in Kampala offer a premium of 23 per cent in housing values compared to freeholds. This could be due to a lack of formal systems for the assessment of leasehold premium and ground rent charges, an arrangement which can offer utility to the lesse at the expense of lessor, thereby making leaseholds popular on the market, or the developers’ lack of information on the benefits of freehold causing them to value leaseholds higher than freeholds. Similarly, private mailo tenure offers a 12 per cent premium in housing values compared to freeholds. There is no significant impact of Kabaka’s mailo tenure on housing values. When compared to private mailo, public leaseholds offer an 11 per cent premium in housing values. Practical implications – There is a need to advance leasehold as the urban land tenure for Uganda, disentangle multiple-layers of ownership on mailo land and roll out the land fund to enhance growth of the housing market in Kampala. Originality/value – This paper is the first of its kind to empirically examine the impact of mailo land tenure on housing values. Findings provide useful insights for investors and policymakers in the housing sector in Uganda.

Hedonic housing prices and environmental quality in Lebanon

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Walid Marrouch ◽  
Nagham Sayour
Keyword(s):  
Air Pollution ◽  
Housing Prices ◽  
Hedonic Pricing ◽  
Hedonic Model ◽  
Functional Regression ◽  
Data Set ◽  
Content Type ◽  
Local Air Pollution ◽  
Variable Bias ◽  
The Impact

Purpose This study aims to examine the impact of local air pollution on housing prices in Lebanon. Design/methodology/approach The authors apply a hedonic pricing approach using a unique data set from Lebanon. To account for non-linearities in pricing, the authors use three different functional regression forms for the hedonic model approach. The authors also deal with potential omitted variable bias by estimating a hedonic frontier specification. Findings The authors find that, in all specifications, air pollution negatively and significantly affects housing prices. The estimated marginal willingness to pay for a one microgram per cubic meter change in particulate matter (PM10) concentration ranges between 2.88% and 3.18% of mean housing prices. The authors also provide evidence of a negative pricing gradient away from the city center, landing support for the monocentric urban development hypothesis. Research limitations/implications Given the lack of a data set linking household socioeconomic characteristics with housing data, the authors only consider the first-stage hedonic model. Practical implications The proposed hedonic pricing regression approximates a housing pricing equation that can be used by policymakers. Social implications The findings suggest that pollution is a significant factor in household behavior in Lebanon. Originality/value This paper adds to the scant literature studying the effects of air pollution on housing prices in developing countries. To the best of the authors’ knowledge, this is the first paper to study the impact of pollution on housing prices in a country in the Middle East and North Africa Region.

scholarly journals Spatially Delineated Public Goods and Spatially Located Public Bads: A Hedonic Approach to Measuring Urban Revitalization

2011 ◽  
Vol 40 (3) ◽  
pp. 360-374 ◽  
Author(s):  
John Brown ◽  
Jacqueline Geoghegan
Keyword(s):  
Public Goods ◽  
Housing Prices ◽  
Urban Revitalization ◽  
Hedonic Model ◽  
High Performing ◽  
Tax Assessment ◽  
Gis Data ◽  
Public Bads ◽  
The Impact ◽  
Hedonic Approach

A regression discontinuity approach is used to measure the impact of public-goods creating programs in a declining inner city neighborhood of Worcester Massachusetts. Using GIS data, we develop a hedonic model of residential sales, using a parcel-level GIS tax assessment and land use database linked to property sales data for the years 1988 through 2007, to test the effect of the creation of a new high-performing public school, as well as other locational amenities and disamenities on neighborhood housing prices, by comparing properties adjacent to either side of the school catchment area boundary.

scholarly journals EPC Labels and Building Features: Spatial Implications over Housing Prices

2021 ◽  
Vol 13 (5) ◽  
pp. 2838
Author(s):  
Alice Barreca ◽  
Elena Fregonara ◽  
Diana Rolando
Keyword(s):  
Real Estate ◽  
Spatial Data ◽  
Housing Stock ◽  
Housing Prices ◽  
Energy Performance ◽  
Ordinary Least Squares ◽  
Real Estate Market ◽  
Hedonic Price ◽  
Spatial Error ◽  
The Impact

The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.

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