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Impact of the urban landscape on residential housing prices in Valencia, Spain
Abstract
In this paper, a hedonic model will be developed to analyse the characteristics that determine the price of a dwelling. On the one hand, these include physical features such as size, floor or age, and others such as common areas. On the other hand, there is the location, because the position of the building has to do with urban landscape and environmental attributes. This latter case is especially interesting because this area of the city has been developed in a period of no longer than fifteen years, since it has grown up around a recently completed architectural and socio-cultural complex that has become an emblematic landmark of the metropolitan area: the City of Arts and Sciences (Santiago Calatrava, Architect). It is located in the old riverbed, which is nowadays integrated within the urban planning of the city, and as well as its cultural, social and architectural value it is also important from an environmental point of view, since it is surrounded by green spaces, bodies of water and a number of other amenities that improve the quality of urban life for local citizens. Primary market research was conducted and then data were carefully checked and selected to obtain the most accurate information possible. The model shows which variables are significant to predict the sale price and it also reflects how important the location of the building is in the price of the property, which in this case refers to its proximity to the riverbed and its views.
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A hedonic model was developed to analyze the market for undeveloped forest land in Minnesota. Variables describing in situ conditions, locational characteristics, buyer perceptions and intentions, and transactional terms were tested for their influence on sale price. The independent variables explained 67% of the per hectare sale price variation. Water frontage, road access and density, absentee ownership, future intentions, and financing arrangements had large, positive influences on price. Lack of a real-estate agent and agricultural land in the vicinity of the parcel had negative influences. A parcel's merchantable timber volume was not a significant predictor of price.
To support decision-making in landscape management and planning policies in urban built-up areas, a formal framework is needed to evaluate urban landscapes objectively and clarify the economic values of landscape amenity. This paper proposes a framework to address this issue. The framework consists of three parts: conducting a standardized landscape survey, extracting critical evaluation factors with a principal component analysis, and then identifying the effect of these factors on land prices with hedonic pricing models. Data from the cities of Tokyo and Kitakyushu were used, which are typical of large metropolitan areas and medium-sized cities in Japan. In each city, some 200–300 transacted vacant sites, designated for residential development, were chosen as samples. The results of principal component analysis and hedonic regression analysis suggested that, in either city, the compatibility of the buildings and the greenery of the neighborhood were distinctively perceived; these factors significantly influenced land prices, and the marginal effects were similar for both cities. The results empirically proved the stableness of the proposed framework and suggested the possibility of generalizing the framework in urban built-up areas. In addition, some important policy implications were provided by the analysis. The outcomes indicate that programs should be provided to motivate residents to preserve or create landscape amenity cooperatively, and justify planning policies to encourage neighborhood-based cooperation for landscape improvement. With the impact of landscape amelioration or damage being elucidated, it also makes it possible to adjust benefits among concerned people, and may help public landscape management sectors optimize their budget plans.
This article presents a hedonic analysis of crude oil, the price and quality of which varies considerably from one stream to another. Different from common hedonic applications in the literature, the estimated implicit prices for the physical characteristics of crude oil have a clear interpretation: it reflects the market's valuation, but not the production cost of the characteristics, for crude oil characteristics are determined by nature. Crude oil hedonic price equations are found to be non-linear in the major physical characteristics, indicating that a refiner's valuation of crude quality depends on its refining technology. The estimation results yield insights into the current pricing system of the world crude oil market.
The purpose of this paper is to explore the factors that affect housing prices in Istanbul. A hedonic price model is employed in order to examine housing price determinants with respect to property characteristics, socio-economic characteristics, neighbourhood quality characteristics, and locational factors. The results reveal that housing prices are affected by these factors: living area size, being in a low storey building, being in a secured site (with swimming pool and garage), and age of the building. In addition to these determinants, the length of time the inhabitants have lived in Istanbul, the average income of the household, neighbour satisfaction and earthquake risk of the area have effects on the residential prices in Istanbul. Further research is suggested by constructing a second model that includes neighbourhood dummy variables as a proxy for submarkets, and a multi-level modelling framework will be employed in order to analyse the urban housing submarket system.
The urban housing market of China has been transformed since the 1980s from a centrally-planned to a free-market system. This study aimed to (1) investigate the position of outdoor environmental quality in house-buyers’ preferences; (2) assess monetary values attributed to environmental externalities by the hedonic pricing method (HPM); and (3) test the applicability of HPM in China. The study area was Guangzhou, the major city of south China with a booming real-estate market. A questionnaire survey was conducted with households in new residences sold in 2004. The main buying motives were improving living quarters and floor area. Security concerns and a preference for high-rise buildings were somewhat unexpected. Good outdoor environment, including green space provision, proximity to parks, and views of green space and water, carried significant hedonic values. Differences between the submarkets of old and new towns were found; new town households expected apartments in high-rise blocks, exclusive residential land use, and views of green space, while old town households preferred proximity to shopping areas and workplaces, green space within the development and proximity to nearby parks. The findings could help to fine tune the developing housing market to match supply with demand in quality terms. Values accorded to environmental attributes could justify funding for urban green spaces and nature conservation. The study verified the applicability of HPM to the housing context in China.
This paper estimates models of social interactions within residential neighborhoods using data on neighborhood clusters for standard metropolitan areas in the United States from the American Housing Survey for 1985 and 1989. It examines effects of social interactions in the form of reaction functions for homeowners' valuation of their properties at the level of the immediate residential neighborhood, with neighborhoods consisting of a randomly chosen dwelling unit and about ten nearest neighbors.The paper identifies the effect of endogenous social interactions to be significant and large, ranging from 0.587 to 0.770, and much more important than the dynamic (autoregressive) structure of the model when both variables are present and both are significant. The interactive regressions that it reports improve upon commonly used hedonic regressions as well. The paper provides empirical support for the notion, common in the real estate world, of the importance of neighboring properties in property valuations.
This paper addresses the following question: Why do island prices vary across locations? To answer this question, an econometric model is developed to explain differences in island prices with respect to their characteristics (distance from the coast, size, economic and political situation of the membership State). The findings suggest that an island price is likely to increase with its size and temperature. Similarly, islands which exhibit greater geographical isolation tend to be more highly valued. Moreover, the price of islands increases in response to an increase in per capita income and population density of the States to which they belong, reflecting the importance of the public good and externality dimension embodied in real estate parcels. On the contrary, political instability and war-proneness of the membership States have no effect on an island price.
Geographic information systems (GIS) provide powerful tools for storing and manipulating large amounts of information on spatial relationships. However, applications of GIS in urban economics and real estate are infrequent. As a symptom of this, there is often little relation between the extensive literature on residential mortgages and the housing markets literature. The purpose of this paper is to discuss the potential contributions GIS could make to urban economic and real estate economic research and open debate on GIS as applied to these fields. Beginning with theory, we determine how GIS can be useful to researchers and what is needed from future GIS. A GIS is a database management system with a spatial reference. It is capable of providing researchers with excellent control over spatial relationships. For example, one can “geocode” the housing transactions used for hedonic pricing studies. The process of geocoding assigns a latitude and longitude coordinate for each transaction. This enables much more elaborate spatial analysis than with traditional techniques such as using a ruler on a paper map. For instance, GIS allows the hedonic pricing literature to be reworked using spatial autoregressive analysis (SAR). SAR models need to repeatedly calculate large numbers of distances from each observation to every other observation within a given radius. In this context, GIS software is primarily useful for assigning a latitude and longitude location to each observation based on the best of street, ZIP, or ZIP+4. These geocoded observations may then be exported to other software for batch processing of SAR statistics. Future development of GIS software should eliminate the need for multiple software packages. Likewise, road distances with flexible weights (e.g., depending on type of road or on construction delays) awaits further software development.