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Focuses on the effect of both proximity and size of shopping centres on surrounding residential property values, using hedonic modelling. States that the data bank consists of a subset of some 4,000 single‐detached, owner‐occupied housing units transacted all over the Quebec Urban Community territory between January 1990 and December 1991. Tests several functional forms and uses up to 60 descriptors. Reveals that in line with previous studies, findings indicate that shopping‐centre size exerts a positive contributory effect on values; they also tend to confirm the non‐monotonicity of the price‐distance function. Concludes that, in that respect, resorting to the gamma function for distance variables yields most interesting results and provides consistent estimates of optimal distances for various shopping‐centre size categories.

Automated valuation models have been in use at least for the last 50 years in both academia and practice, while automated valuation recently re-emerged as very important with the rise of digital infrastructure. The current state of the art, therefore, justifies the dual contributions of this paper: organising existing knowledge and providing a new framework.

This paper provides much-needed analysis and synthesis of the accumulated body of knowledge by proposing an updated classification of automated valuation approaches based on two criteria, and a taxonomy adapted to new trends. The latter requires a paradigm shift from models to automated valuation systems. Both classification and taxonomy arose after literature review.

This paper provides a framework for an explicit context under which automated valuation is carried out. To do so, authors propose a definition of automation valuation systems; contextualise the differences among theories, approaches, methods, models and systems present in automated valuation and introduce a classification of automated valuation approaches and a non-hierarchical taxonomy of automated valuation systems.

Perhaps, a systematic literature review process instead of a selective list of 100 references could additionally validate the proposed classification and taxonomy.

The new framework, underlying various dimensions of the automated valuation process, can help practitioners surpass judging models based purely on their predictive accuracy. Also, the automated valuation system is a more generic term that can better accommodate future research coming from a multitude of disciplines, more diverse business areas and enlarged variety of practical users.

This is the first paper that develops a taxonomy of automated valuation systems.

The purpose of this paper is to address the heterogeneity of real estate assets with regard to investment risk measurement, with Paris’ apartment market as a case study.

Quantile regression is used to handle the fact that willingness to pay for housing attributes may vary greatly over both space and asset value categories. The method is alternately applied on central and peripheral districts of Paris, or “arrondissements”, with hedonic indices built for nine deciles over a 17-year period (1990-2006). Portfolio allocation is subsequently analysed with deciles being the assets.

The findings suggest that during the slump, peripheral districts show better resilience and define the efficient frontier while also exhibiting a lower volatility. In addition, higher returns are observed for lower-priced apartments, both central and peripheral. During the recovery and boom stages of the cycle, the highest returns are experienced for the cheapest apartments in central locations, whereas upper-priced, centrally located units yield the lowest returns.

The originality of this research resides in the application of quantile regression in a real estate investment and risk management context. The methodology may raise individual investors’ and practitioners’ attention, especially index providers’.

The current state-of-the-art recognises three traditional valuation approaches. The current division is not sufficient to explain systematically all features that drive the development and usage of automated valuation models.

This practice briefing reviews existing valuation approaches, their pros and cons and more critical other automated valuation aspects or features; both based on a literature review.

This paper discusses and lists the six critical aspects or features, besides the valuation approaches.

This paper reveals the list of aspects or features that are important to consider when designing an automated valuation model.

This practice briefing discusses the inclusion of a multitude of aspects when considering an automated valuation model design.

This paper is an attempt to bridge the gap between, on the one hand, the mobility behaviour of households and their perception of accessibility to urban amenities and, on the other hand, house price dynamics as captured through hedonic modelling.

In order to analyse the mobility behaviour of individuals and households, their sensitivity to travel time from home to service places is estimated so as to assess their perceived accessibility, using “subjective” indices based on actual trips, as reported in the 2001 origin‐destination survey designed for Quebec City. For comparative purposes, both objective and subjective accessibility indices based, in the former case on observed travel times and, in the latter case on fuzzy logic criteria, are computed and used as a complement to a centrality index in a hedonic model of house prices.

Findings indicate that there are statistically significant differences in the way accessibility is structured depending on trip purposes and household profiles. They also suggest that, while an objective measure of accessibility yields good results, resorting to subjective, and more comprehensive, accessibility indices derived from fuzzy logic provides greater insight into the understanding of commuting patterns and travel behaviour of people.

Better understanding the complexity of individuals’ and households’ mobility behaviour should result in more adequate initiatives and decisions being taken by transportation and city planning authorities.

Accessibility to jobs and services has long been known as a major determinant of urban, residential and non residential, rents. Yet, it is more often than not assumed to derive from a rather straightforward process, which this paper shows is not the case.

This paper investigates the analytical potential of factor analysis for sorting out neighbourhood and access factors in hedonic modelling using a simulation procedure that combines GIS technology and spatial statistics. An application to the housing market of the Quebec Urban Community (575,000 in population; study based on some 2,400 cottages transacted from 1993 to 1997) illustrates the relevance of this approach. In the first place, accessibility from each home to selected activity places is computed on the basis of minimum travelling time using the TransCAD transportation‐oriented GIS software. The spatial autocorrelation issue is then addressed and a general modelling procedure developed. Following a five‐step approach, property specifics are first introduced in the model; proximity and neighbourhood attributes are then successively added on. Finally, factor analyses are performed on each set of access and census variables, thereby reducing to six principal components an array of 49 individual attributes. Substituting the resulting factors for the initial descriptors leads to high model performances, controlled collinearity and stable hedonic prices, although remaining spatial autocorrelation is still detected in the residuals.

This research paper investigates the effect of drinking water quality on property values in Charlesbourg, a major municipality (70,000 inhabitants) of the Quebec City region where repeated water‐related health problems were experienced in 1990 and 1991. In this paper, 807 bungalow sales are sampled from the data bank of the Quebec Urban Community (QUC) Appraisal Division, and environmental information pertaining to local drinking water quality levels supplements data on physical, neighbourhood and access attributes. Our findings indicate that water‐related health hazards exert a detrimental and measurable impact on higher property values, with the average duration of the warning period per sector clearly emerging as the dominant factor. More precisely, market segmentation suggests that the higher the price of the property, the sharper the decline in market value because of this factor. In the current case study, the most severely affected properties of the upper third segment of the market experienced drops in value ranging from 5.2 to 10.3 percent of mean sale price.

This paper presents a procedure for considering interactions of neighbourhood quality and property specifics within hedonic models of housing price. It handles interactions between geographical factors and the marginal contribution of each property attribute for enhancing values assessment. Making use of simulation procedures, it is combining GIS technology and spatial statistics to define principal components of accessibility and socio‐economic census related to transaction prices of single‐family homes. An application to the housing market of the Quebec Urban Community (more than 3,600 bungalows transacted in 1990 and 1991) illustrates its usefulness for building spatial hedonic models, while controlling for multicollinearity, spatial autocorrelation and heteroskedasticity. Distance‐weighted averages of each property attribute in the neighbourhood and interactions of property attributes with each principal component are used to detect any spatial effect on sale price variations. This first‐stage spatial hedonic model approximates market prices, which are then used in order to compare “expected” and actual property tax amounts, which are added to obtain a second‐stage model incorporating fiscal effects on house values. Interactions between geographical factors and property specifics are computed using formulae avoiding multicollinearity problems, while considering several processes responsible for spatial variability. For each property attribute, they define sub‐models which can be used to map variations, across the city, of its marginal value, assessing the cross‐effect of geographical location (in terms of neighbourhood profiles and accessibility to services) and its own valuation parameters. Moreover, this procedure distinguishes property attributes, exerting a stable contribution to value (constant over the entire region) from those whose implicit price significantly varies over space.

Spatial autocorrelation in regression residuals is a major issue for the modeller because it disturbs parameter estimates and invalidates the reliability of conclusions drawn from models. The purpose of this paper is to develop an approach which generates new spatial predictors that can be mapped and qualitatively analysed while controlling for spatial autocorrelation among residuals.

This paper explores an alternate approach using a Fourier polynomial function based on geographical coordinates to construct an additional spatial predictor that allows to capture the latent spatial pattern hidden among residuals. An empirical validation based on hedonic modelling of sale prices variation using a large dataset of house transactions is provided.

Results show that the spatial autocorrelation problem is under control as shown by low Moran's I indexes. Moreover, this geo‐statistical approach provides coefficients on environmental amenities that are still highly significant by capturing only the remaining spatial autocorrelation.

The originality of this paper relies on the development of a new model that allows considering, simultaneously spatial and time dimension while measuring the marginal impact of environmental amenities on house prices avoiding competition with the weight matrix needed in most spatial econometric models.

Both hedonics and the traditional sales comparison approach are derived from a similar paradigm with respect to how prices, hence market values, are determined. While the hedonic approach can provide reliable estimates of individual attributes' marginal contribution, it may – unlike the sales comparison approach – underestimate the prominent influence that surrounding properties exert on any given nearby housing unit and sale price. This paper seeks to develop a simple method for reconciling the two approaches within a rigorous conceptual and methodological framework.

Peer effect models, an analytical device developed, and mainly used, by labour economists, are adapted to the hedonic price equation so as to incorporate nearby properties' influences, thereby controlling for non‐observable neighbourhood effects. In addition to basic, intrinsic, building and land attributes, the ensuing model accounts for three types of effects, namely endogenous interactions effects (i.e. comparable sales influences, or peer effects), exogenous, or neighbourhood, effects and, finally, spatial autocorrelation effects.

Preliminary findings suggest that integrating peer effects in the hedonic equation allows bringing out the combined impacts of endogenous, exogenous and spatially correlated effects in the house price determination process, with spatial autocorrelation of model residuals being significantly reduced, even without resorting to a spatial autoregressive procedure.

Further investigation is still needed in order to find out which submarket delineation should be used to obtain optimal model performances.

The paper leads to the conclusion that the comparable sales approach, as used in traditional appraisal practice, is valid, although its application is typically flawed by the too small sample size generally used by appraisers. Further investigation is still needed, however, in order to find out which submarket delineation should be used to obtain optimal model performances. This raises the paramount question as to whether the peer effect variable is adequately measured and addresses the tricky issue of kernel determination in spatial statistics and related applications, such as GWR.