Contextualized property market models vs. Generalized mass appraisals: An innovative approach

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies

A Neural-CBR System for Real Property Valuation

In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However, individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case- Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case- Based Reasoning systems

Respondent uncertainty in contingent valuation: the case of whale conservation in Newfoundland and Labrador

In this paper we investigate the issue of respondent uncertainty in contingent valuation studies while estimating the willingness to pay for a whale conservation program o¤ the coasts of Newfoundland and Labrador. We use data from a phone survey administered to a sample (N=614) of adult Canadians, proposing a policy consisting of subsidizing and enforcing the use of acoustic devices that would reduce the likelihood that whales become entangled in �shing nets. A follow-up question asked respondents how certain they were about their answer to the main dichotomous-choice question, which allows us to investigate how the treatment of uncertainty a¤ects value measures. A mean willingness to pay of about $81/year per respondent is estimated when accounting for the degree of certainty with which respondents expressed their willingness to pay. We also analyze payment vehicle e¤ects using a split-sample approach whereby some respondents were asked a dichotomous-choice question about a tax contribution while others were asked about a voluntary donation instead.contingent valuation; whales; preference uncertainty; dichotomous choice; payment vehicle; willingness to pay

Testing the Temporal Stability of Accessibility Value in Residential Hedonic Prices

Purpose – This paper bridges the gap between, on the one hand, supply-driven (urban form and transportation networks) and demand-driven (action-based) accessibility to urban amenities and, on the other hand, house price dynamics as captured through panel hedonic modelling. It aims at assessing temporal changes in the valuation of accessibility, while ordering households’ priorities among access to labour market, schools and shopping outlets. Design/methodology/approach – Several indexes are built using a methodology developed by Thériault et al. (2005, published in Journal of Property Investment and Finance). They integrate car-based travel time on the road network (using GIS), distribution of opportunities (activity places) within the city, and willingness of persons to travel in order to reach specific types of activity places (mobility behaviour). While some measure centrality (potential attractiveness considering travel time, population and opportunities) others consist of action-based indexes using fuzzy logic and capture the willingness to travel in order to reach actual specific activity places (work places, schools, shopping centres, groceries). They summarise suitable opportunities available from each neighbourhood. Rescaled indices (worst - to 100 - best) are inserted simultaneously into a multiplicative hedonic model of single-family houses sold in Quebec City during years 1986, 1991 and 1996 (10,269 transactions). Manipulations of accessibility indexes are developed for ordering their relative impact on sale prices and isolate effects of each index on the variation of sale price, thus providing proxies of households’ priorities. Moreover, a panel-like modelling approach is used to control for changes in the valuation of each property-specific, taxation or accessibility attribute during the study period. Findings – This original approach proves efficient in isolating the cross-effects of urban centrality from accessibility to several types of amenities, while controlling for multicollinearity and heteroscedasticity. Results are in line with expectations. While only a few property-specific attributes experience a change in their marginal contribution to house value during the study period, all accessibility indexes do. Every single accessibility index has a much stronger effect on house values than centrality (which is still marginally significant). When buying their home, households put more emphasis on access to schools than they put on access to the labour market, which in turn, prevail over accessibility to either shopping centres or, finally, groceries. The ordering is rather stable but the actual valuation of a specific amenity may change over time. Practical implications – Better understanding the effect of accessibility to amenities on house values provides guidelines for choosing among a set of new neighbourhoods to develop in order to generate optimal fiscal effects for municipalities. It could also provide guidelines for decision making when improving transportation networks or locating new activity centres.

Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies

This paper focuses on the problem of applying rough set theory to mass appraisal. This methodology was first introduced by a Polish mathematician, and has been applied recently as an automated valuation methodology by the author. The method allows the appraiser to estimate a property without defining econometric modeling, although it does not give any quantitative estimation of marginal prices. In a previous paper by the author, data were organized into classes prior to the valuation process, allowing for the if-then, or right “rule” for each property class to be defined. In that work, the relationship between property and class of valued was said to be dichotomic.mass appraisal; property valuation; rough set theory; valued tolerance relation

Is it worth identifying service employment (sub)centres when modelling apartment prices?

The use of the attributes of the central business district and several subcentres instead of the characteristics of all the land parcels or zones can be seen as a higher level of analysis in real estate valuation. However, old technological limitations on considering smaller territorial units are being successfully overcome. The question is whether or not we still need generalisation, i.e. to identify urban centres when modelling real estate prices, or whether it is preferable to operate at a lower spatial level. The application of the traditional approach of identifying centres is compared with an 'objective' centrality index and a 'subjective' accessibility index calculated for each zone. The purpose is to find out which of the three concepts best fits a regression model of apartment prices and provides the best prediction. Both global and geographically weighted ordinary least squares regressions are used as well as spatial lag and spatial error models. We conclude that if a model is spatially weighted or the spatial effects are controlled, it is not that important which of the concepts is applied. Nevertheless, in most cases the highest predictive capacity is obtained with duocentric models.service employment centres ; centrality ; accessibility ; apartment price ; hedonic modelling

Private Real Estate Investment Analysis within a One-Shot Decision Framework

Land development is a typical one-shot decision for private investors due to the huge investment expense and the fear of substantial loss. In this paper, a private real estate investment problem is analyzed within a one-shot decision framework, which is used for a situation where a decision is made only once. The one-shot decision framework involves two steps. The first is to identify which state of nature should be focused for each alternative. The second is to evaluate alternatives by using the focused states of nature. In a one-shot decision framework, the behavior of different types of private investors, such as normal, active, passive and more easily satisfied ones, are examined. The analysis provides insights into personal real estate investment and important policy implications in the regulation of urban land development.Private real estate investment; Possibility theory; One-shot decision; Focus points

USING CONTINGENT VALUATION WITH RESPONDENT UNCERTAINTY TO ESTIMATE THE COSTS OF CLIMATE CHANGE PROGRAMS: AN APPLICATION TO CANADIAN LANDOWNERS

Using a survey of western Canadian agricultural landowners, we examine the cost and viability of two distinct afforestation options for carbon-uptake purposes. Responses to two separate, but most-likely related willingness to accept compensation questions are elicited using the contingent valuation method. Respondents then select the level of certainty with which they believe their responses were given. This paper provides a framework for estimation of the bivariate model with certainty and a modification of the model to incorporate uncertainty based on Li and Mattson's approach to preference uncertainty. While highly preliminary results are given for the bivariate model with certainty, applications of both models will be presented at the 2003 AAEA Meetings.Environmental Economics and Policy, Resource /Energy Economics and Policy,

Identifying Real Estate Opportunities using Machine Learning

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.Comment: 24 pages, 13 figures, 5 table

Organic farming and multicriteria decisions: An economic survey

Organic food production is a sphere where decision making is multi-facetted and complex. This applies to producers, political decision makers and consumers alike. This paper provides an overview of the economic methods that can aid such multi criteria decision making. We first provide an outline of the many different Multi-Criteria Analysis (MCA) techniques available and their relative advantages and disadvantages. In addition, theoretical and practical problems related to the use of Cost-Benefit Analysis (CBA) and MCA respectively are briefly discussed. We then review the MCA literature on case studies on organic farming. Based on this review we provide directional markers for future research where MCA may possibly be applied and adapted in order to provide useful knowledge and support for decision makers in the context of organic farming