Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich

This article examines the spatially varying effect of age on single-family house (SFH) prices. Age has been shown to be a key driver for house depreciation and is usually associated with a negative price effect. In practice, however, there exist deviations from this behavior which are referred to as vintage effects. We estimate a spatially varying coefficients (SVC) model to investigate the spatial structures of vintage effects on SFH pricing. For SFHs in the Canton of Zurich, Switzerland, we find substantial spatial variation in the age effect. In particular, we find a local, strong vintage effect primarily in urban areas compared to pure depreciative age effects in rural locations. Using cross validation, we assess the potential improvement in predictive performance by incorporating spatially varying vintage effects in hedonic models. We find a substantial improvement in out-of-sample predictive performance of SVC models over classical spatial hedonic models.

Which professional skills value more under digital transformation?

PurposeThis study seeks to explore the demand side of the labour market influenced by the digital revolution. It aims at identifying the new composition of skills and their value as implicitly manifested by employers when they look for the new labour force. The authors analyse the returns to computing skills based on text mining techniques applied to the job advertisements.Design/methodology/approachThe methodology is based on the hedonic pricing model with the Heckman correction to overcome the sample selection bias. The empirical part is based on a large data set that includes more than 9m online vacancies on one of the biggest job boards in Russia from 2006 to 2018.FindingsEmpirical evidence for both negative and positive returns to computing skills and their monetary values is found. Importantly, the authors also have found both complementary and substitutional effects within and between non-domain (basic) and domain (advanced) subgroups of computing skills.Originality/valueApart from the empirical evidence on the value of professional computing skills and their interrelations, this study provides the important methodological contribution on applying the hedonic procedure and text mining to the field of human resource management and labour market research.

Spatiotemporal effects of proximity to metro extension on housing price dynamics in Manhattan, New York City

Investment in public transportation such as a metro line extension is often capitalized partially into housing values due to the spatiotemporal effects. Using housing transaction data from 2014 to 2019, this paper studies the Second Avenue Subway or Q-line extension in New York’s City’s Manhattan borough. Multiple metro station catchment areas were investigated using spatial autocorrelation-corrected hedonic pricing models to capture the variation of housing price dynamics. The results indicate that properties in closer proximity to the Q-line extension received higher price discounts. The effect varied by occupancy type and building form: condominiums experienced the highest price discount, while walk-up and elevator co-ops experienced a price premium. After controlling for location variations, we observed price discounts on the westside and price premiums on the eastside of the Q-line. Residential properties within 150 m west to the Q-line extension received the highest price discount post operation, while on the eastside, properties in the same proximity received the highest price premium. The anticipation effect varies by distance to metro extension stations, both before and after the operation of metro line extension. We discuss the disruption of metro construction on the housing market depending on housing type, location variation, and changes over time.

Clustering Analysis with Embedding Vectors: An Application to Real Estate Market Delineation

Although clustering analysis is a popular tool in unsupervised learning, it is inefficient for the datasets dominated by categorical variables, e.g., real estate datasets. To apply clustering analysis to real estate datasets, this study proposes an entity embedding approach that transforms categorical variables into vector representations. Three variants of a clustering algorithm, i.e., the clustering based on the traditional Euclidean distance, the Gower distance, and the embedding vectors, are applied to the land sales records to delineate the real estate market in Gwacheon-si, Gyeonggi province, South Korea. Then, the relevance of the resultant submarkets is evaluated using the root mean squared errors (RMSE) obtained from a hedonic pricing model. The results show that the RMSE in the embedding vector-based algorithm decreases substantially from 0.076-0.077 to 0.069. This study shows that the clustering algorithm empowered by embedding vectors outperforms the conventional algorithms, thereby enhancing the relevance of the delineated submarkets.

VALUATION OF MARKET RENTAL VALUE OF A PLANNED DORMITORY USING HEDONIC PRICING METHOD: A CASE STUDY OF POLYTECHNIQUE OF STATE FINANCE STAN, INDONESIA

This study determines the attributes that affect the market rental value of dormitories using the Hedonic Pricing Model. The proportional stratified random sampling technique was used to obtain data from 1,292 PKN STAN students in levels 1 to 3, which was analyzed using the SPSS statistical application. Based on the calculation, the dormitory value varies between IDR11,719,521 (RM3,424.82) to IDR15,482,242 (RM4524,41). The determinants that have a significant positive effect on dormitory value are bathroom location, average remittances per month, earnings per month, room size, gender, and origin, while the type of residence attribute has a negative correlation effect. The results of this study will be beneficial inputs for the PKN STAN in determining the market rental value, the quality of buildings and facilities are in accordance with the market preference.

A REVIEW OF SPATIAL ECONOMETRICS IN EXPLICIT LOCATION MODELLING OF COMMERCIAL PROPERTY MARKET

‘Location, location, location’ is a real property parlance mostly used to describe the influence of location in the property market. Location is mainly considered as the most significant influencer of commercial property prices. Location is modelled traditionally using hedonic pricing model by either proxy location dummies or distances relative to other neighbourhood features. This was shown to be inadequate due to spatial autocorrelation and heterogeneity inherent in spatial data, which jeopardises the estimates' consistency. Consequently, spatial econometrics is used to explicitly model location into property pricing by controlling spatial effects of autocorrelation and heterogeneity. Housing studies dominate the use of this approach with limited application in the commercial property market. This paper reviewed spatial econometrics and found that the commercial property market exhibits significant spatial dependence and heterogeneity. Accounting for such effects improves model accuracy significantly. It, therefore, recommends increase use of spatial econometrics in commercial property market modelling.

Sorting in an urban housing market—is there a response to demographic change?

Using Dortmund as a case study we analyse whether rents and housing prices responded to local demographic change in a German city between 2007 and 2016. In a two-step analysis based on a spatial autoregressive hedonic pricing model and a discrete choice model of housing location we find that during the study period as a whole, higher local mortality induced a negative effect on apartment prices and rents. Yet, the neighbourhood effects of local ageing vary across sub-city districts. Most prominently, the study period was characterised by a strong and rising desire to purchase or rent housing in the vicinity of the city centre. Furthermore, prices for owner-occupied apartments and houses increased rapidly in the more well-off southern part of the city and particularly in a previously declining community, where a large-scale urban regeneration and environmental upgrading project has been implemented since 2011. The characteristics of households likely to move to this neighbourhood switched from low to high income.

Geospatial Heterogeneity in Monetary Value of Proximity to Waterfront Ecosystem Services in the Gulf of Mexico

Open spaces, including waterfront areas, are critical to coastal communities and provide many benefits, including recreation opportunities, economic development, ecological benefits, and other ecosystem services. However, it is not clear how values of waterfront ecosystem services vary across geographical areas which prevents development and adoption of site-specific natural resource conservation plans and suitable long-term land management strategies. This study estimated the monetary value of distance to different waterfront types in coastal counties of Mississippi and Alabama (U.S.) using a geographically weighted regression (GWR) approach as an extension to a traditional hedonic pricing method (HPM). In addition, the study utilized publicly available data from the U.S. Census Bureau instead of certified rolls of county property assessors and Multiple Listing Service (MLS) data which can be costly and difficult to obtain. Residents valued most waterfront types which was reflected in greater assessed prices for houses in proximity to these waterfronts. However, the value of ecosystem services associated with waterfronts differed geospatially. The marginal implicit prices ranged from −$6343 to $6773 per km depending on a waterfront type. These estimates will be useful to city developers, land-use planners, and other stakeholders to make more informed and balanced decisions related to natural resource preservation associated with coastal areas, land-use planning, and zoning. In addition, information from this study can be used in developing healthy living environments where local economy can benefit from increased property tax revenues associated with waterfronts and their ecosystem services.

Willingness to Pay in Hedonic Pricing Models

The value of a differentiated product is simply the sum of its parts. This concept is easily observed in housing markets where the price of a home is determined by the underlying bundle of attributes that define it and by the price households are willing to pay for each attribute. These prices are referred to as implicit prices because their value is indirectly revealed through the price of another product (typically a home) and are of interest as they reveal the value of goods, such as nearby public amenities, that would otherwise remain unknown. This concept was first formalized into a tractable theoretical framework by Rosen, and is known as the hedonic pricing method. The two-stage hedonic method requires the researcher to map housing attributes into housing price using an equilibrium price function. Information recovered from the first stage is then used to recover inverse demand functions for nonmarket goods in the second stage, which are required for nonmarginal welfare evaluation. Researchers have rarely implemented the second stage, however, due to limited data availability, specification concerns, and the inability to correct for simultaneity bias between price and quality. As policies increasingly seek to deliver large, nonmarginal changes in public goods, the need to estimate the hedonic second stage is becoming more poignant. Greater effort therefore needs to be made to establish a set of best practices within the second stage, many of which can be developed using methods established in the extensive first-stage literature.