Добірка наукової літератури з теми "Housing appraisal modelling"

We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal.

Abstract The article is a voice in the debate on the scope of the application of statistical methods in real estate appraisal, written from the comparative perspective. It presents the results of an illustrative valuation of housing units with the use of databases of various sizes, constructed on the basis of publicly available data from the register of property prices and values. Against this background, the article presents an analysis of differences between the objectives and published results of valuations, which exemplify broadly understood property price modelling or property value modelling, as well as of activities focused around appraising a specific object. The conducted experiments demonstrated that, for the purposes of real estate appraisal itself, the selection of data is more useful than searching for a price model.

PurposeThe feasibility and viability appraisal technique is becoming increasingly crucial in the planning systems, theory, applications and outputs for property development and project investments. This paper aims to account for the findings of the practices associated with risk in the feasibility and viability appraisal process. Also, it examines the need for a practical framework for conducting a feasibility and viability appraisal, which can be employed by estate surveyors and valuers in NigeriaDesign/methodology/approachThis study adopted purposive sampling techniques to administer 240 sets of questionnaires, out of which 210 sets were well-thought-out to be useable for the analysis after data screening. Statistical package for social sciences (SPSS), structural equation modelling (SEM) and analysis of movement structures (AMOS) were the main analytical tools used to carry out the reliability test, normality test, exploratory factor analysis, confirmatory factor analysis, measurement and structural model.FindingsThe analysis results indicated that the P-values of the various forms of concepts of risks in feasibility and viability appraisal process (preparation) for property development and the investment market was statistically significant: technological factor - 0.000; political factor- 0.000 and economic factor- 0.000. However, a non-significant effect was found with socio-environmental factors on the preparation of housing development appraisal with P-value 0.155, and that risk management is neither holistically implemented in the feasibility and viability appraisal process nor extensively taken into cognisance.Research limitations/implicationsThis paper reports the results of the practices among estate surveyors and valuers in regarding the risk associated in the preparation stages of the feasibility and viability appraisal processPractical implicationsThere are limited studies that suggest risk management factors in the appraisal reports for property development. Although previous studies have identified the risk factors, there is a lack of emphasis on management, which entails identification, assessment, monitoring and control. This study, therefore, recommends the incorporation of risk management into the feasibility and viability appraisal process implemented by estate surveyors and valuers. It is envisaged that the process will protect investors from the potential risk factors associated with investments in property development.Originality/valueThe study highlighted the need for practical or empirical research to be used to assess the significant risk factors that are needed to be reflected in the preparation stages of the feasibility and viability appraisal conduct of estate surveyors and valuers in Abuja, Nigeria.

Purpose – This study aims to investigate the relationship between energy performance and property sale price in the Belfast housing market. How energy efficiency is contributes to sale price and thus appraisal value is of growing concern. The obligatory measurement of energy efficiency in private dwellings seeks to encourage improvements in energy performance. This may be capitalised into property value and may stimulate demand for energy-efficient buildings. However, the relationship between energy performance and property value remains nebulous, complex and under-researched – in part due to data limitations. Design/methodology/approach – Using a hedonic pricing specification, this paper measures the effect of energy performance certificates (EPCs) on residential property value. It examines the relationship between 3,797 residential sales transactions across the Belfast housing market, showing the percentage effect on property value with respect to energy performance. Findings – The results indicate a small but positive relationship between better energy performance and higher selling prices. Nonetheless, the findings point towards strong preference, demand tastes and a complex intra-relationship between EPCs and their capitalisation into property value. Pertinently, the findings point towards any energy-efficient-related price effect affect to be marginal alongside more “quality”-based market behaviours. Research limitations/implications – Analogous with other studies, data deficiencies and a lack of incorporating price determining variables (missing determinants) such as heating type and glazing type introduces omitted variable bias and endogeneity problems within the model structure. Originality/value – This paper contributes to emerging literature and policy debate surrounding the measurement and implementation of energy-efficiency certification through a greater understanding of energy performance characteristics in determining property value.

Purpose Buildings contribute significantly to CO2 production. They are also subject to considerable taxation based on value. Analysis shows that while similar attributes contribute to both value and CO2 production, there is only a loose relationship between the two. If we wish to use taxation to affect policy change (drive energy efficiency behaviour), we are unlikely to achieve this using only the current tax base (value), or by increasing the tax take off this current tax base (unlike extra taxation of cigarettes to discourage smoking, for example). Taxation of buildings on the basis of energy efficiency is hampered by the lack of current evidence of performance. This paper aims to model the now-obligatory (at sale or letting) energy performance certificate (EPC) data to derive an acceptable appraisal model (marked to market, being the EPC scores) and deploys this to the entire population of properties. This provides an alternative tax base with which to model the effects of a tax base switch to energy efficiency and to understand the tax incidence effects of such a policy. Design/methodology/approach The research uses a multiplicative hedonic approach to model energy efficiency utilising EPC holding properties in a UK jurisdiction [Northern Ireland (NI)] as the sample. This model is then used to estimate discrete energy assessments for each property in the wider population, using attributes held in the domestic rating (property tax) database for NI (700,000+ properties). This produces a robust estimate of the EPC for every property in its current condition and its cost-effective improved condition. This energy assessment based tax base is further used to estimate a new millage rate and property tax bill (green property tax) which is compared against the existing property tax based on value to allow tax incidence changes to be analysed. Findings The findings show that such a policy would significantly redistribute the tax burden and would have a variety of expected and some unexpected effects. The results indicate that while assessing the energy performance of houses can be a complex process involving many parameters, much of the explanatory power can be achieved via a relatively small number of input variables, often already held by property tax jurisdictions. This offers the opportunity for useful housing stock modelling – such as the savings possible from power switching. The research also identifies that whilst urban areas display the expected “heat island” effect in terms of energy consumption, urban properties are on average more efficient than suburban/rural properties. This facilitates spatial targeting of policy messages and initiatives. Research limitations/implications Analogous with other studies, data deficiencies introduce the risk of omitted variable bias. Modelling of the energy efficiency in the sample is limited to property attributes that are available for the wider population of properties. While this limits the modelling exercise, it is a perennial issue facing mass appraisal worldwide (where knowledge of the transacted sample attributes generally exceeds knowledge of the unsold properties). That said, the research demonstrates the benefits of sharing data and improving knowledge of the housing stock, as taxation databases would be stronger, augmented with EPC-derived property attributes for example. Originality/value The EPC lead in time for wide residential coverage is likely to be considerable. The paper contributes to emerging literature and policy debate surrounding the effect, performance measurement and implementation of energy efficiency certification, through a greater understanding of the sectorial and geographical dispersion of energy efficiency. It provides high level research to help guide policy and decision-making, identifying key locales where there is more of a physical problem and locations where there is more to gain in terms of targeting energy improvement and/or encouraging behavioural change. The paper also allows a glimpse of the implications of a change towards a taxation regime based on energy efficiency, which contributes to the debate surrounding the “greening” of property based taxes.

Purpose – This paper aims to pose an important starting point for the application of the search-and-matching models to real estate appraisals, thus reducing the “gap” between practitioners and academicians. Due to relevant trading frictions, the search-and-matching framework has become the benchmark theoretical model of the housing market. Starting from the large related literature, this paper develops a simplified approach to modelling the frictions that focuses on the direct relationship between house price and market tightness (a common feature only for the labour market matching models). The characterization of the equilibrium through two main variables simplifies the analysis and allows using the theoretical model for empirical purposes, namely, the real estate appraisals. Design/methodology/approach – This work is both theoretical and empirical. Theoretically, a long-run equilibrium model with a positive share of vacant houses and home seekers is determined along with price and market tightness. Also, the conditions of existence and uniqueness of the steady-state equilibrium are determined. Unlike most of the search-and-matching models in the housing literature, the out-of-the steady-state dynamics are also analyzed to show the stability of the equilibrium. Empirically, to show the usefulness of the theoretical model, a numerical simulation is performed. By using two readily available housing market data – the expected time on the market and the average number of trades – it is possible to determine the key variables of the model: price, market tightness and matching opportunities for both buyers and sellers. Although the numerical simulation concerns the Italian housing market, the proposed model is generally valid, being empirically applicable to all real estate markets characterized by non-negligible trading frictions. Indeed, the proposed model can be used to compare housing markets with different features (concerning the search and matching process), as well as analyse the same housing market in different time periods (because the efficiency of the search and matching process can change). Findings – Several important results are obtained. First, the price adjustment – i.e. the difference between the actual selling price and the price obtained in an ideal situation of frictionless housing market – is remarkable. This means that the sign and the size of the price adjustment depend on the extent of trading frictions in the housing market. Precisely, the higher the trading frictions on the demand side (more buyers and less sellers), the higher the actual selling price (the price adjustment is positive), whereas the higher the trading frictions on the supply side (less buyers and more sellers), the lower the actual selling price (the price adjustment is negative). Accordingly, the real estate appraisers should assess the trading frictions in the housing market before determining the price adjustment. Second, an increase in the number of trades affects the house price only if the time on the market varies. Also, the higher the variation in the time on the market, the larger the house price adjustment. Indeed, the expected time on the market reflects the opportunities to matching for both parties and thus the trading frictions. If the time on the market increases (decreases), the seller will receive less (more) opportunities to match; thus, the actual selling price will be driven downwards (upwards). Originality/value – As far as the authors are aware, none of the existing works in the search and matching literature has considered how to take advantage of this theoretical approach to estimate the house price in the presence of trading frictions in the housing market. Indeed, the proposed theoretical model may be a useful tool for real estate appraisers, as it is able to derive the trading frictions from the time on the market and the number of trades, thus estimating properly the house price.

Abstract Regression methods can be used for the valuation of real estate in the comparative approach. However, one of the problems of predictive modelling is the presence of redundant or irrelevant variables in data. Such variables can decrease the stability of models, and they can even reduce prediction accuracy. The choice of real estate’s features is largely determined by an appraiser, who is guided by his/her experience. Still, the use of statistical methods of a feature selection can lead to a more accurate valuation model. In the paper we apply regularized linear regression which belongs to embedded methods of a feature selection. For the considered data set of real estate land designated for single-family housing we obtained a model, which led to a more accurate valuation than some other popular linear models applied with or without a feature selection. To assess the model’s quality we used the leave-one-out cross-validation.

Drawing upon the human capital theory, this article articulates the impact of different human resource management (HRM) practices on various dimensions of employee competence in the Indian context. The survey method was employed to collect data from 325 banking employees including managers and non-managers employed in the State Bank of India, the Bank of Baroda, the Housing Development Finance Corporation and the Industrial Credit and Investment Corporation of India. The self-competence, team competence and social competence are the dimensions of employee competencies and HRM practices include recruitment and selection, training and development, employee involvement and performance appraisal. The validity and reliability of the variables are evaluated through the confirmatory factor analysis and the hypotheses are tested using structural equation modelling. The study’s analysis revealed mixed results wherein a significant and positive impact was found between some HRM practices and employee competencies and an insignificant impact between other HRM practices and employee competencies. The study’s findings would serve as a guide to the management and policymakers of banks for developing and enhancing the desired employee competencies by investing in HRM practices, thereby better performance at the organization level. This research work enriches the existing literature on HRM and development by empirically validating the HRM-competence linkages in the Indian banking context, where studies of this nature are minimal.

Housing market price prediction is a major and important challenge in economics. Since the 2008 global financial crisis, researchers, economists, and politicians around the world have increasingly drawn attention to the need of better understanding housing market behaviour, since the failure to predict housing market crisis ahead of time had led to catastrophic global damage. On the other hand, around the same time, we have seen the revolution of information technology and artificial intelligence in the last two decades. The advent of powerful cloud and high performance computing systems, big data, and advanced machine learning algorithms have demonstrated new applications and advantages in cutting-edge research and technology areas such as pattern recognition, bioinformatics, natural language processing, and product recommendation systems. Can we make the leap of improving our understanding of housing market behaviour by leveraging these recent advances in artificial intelligence and newly available big data? This is the main theme of the thesis. There is strong motivation to explore the application of data science methods, including new large datasets and advanced machine learning algorithm, to accelerate our understanding of housing market problems for the benefit of the common good. In order to understand housing market behaviour, we divide the problem into two major steps: first, to improve understanding of housing appraisal (at microlevel), which is to predict housing price at the point level given a fixed timeframe; second, to improve understanding of the trend prediction (at macro level), which is to predict the housing price trend for a specific place during a time interval. For these two major steps, we improve upon traditional economic modelling by: • Adding new, non-traditional variables/features to our models, such as location-based Point of Interests, regional economic clusters, qualitative index, searching index, and newspaper articles • Applying machine learning algorithms for data analysis, such as non-linear algorithms, K-Nearest-Neighbour, Support Vector Machine, Gradient Boost, and sentiment analysis Specifically, in Chapter 3, we focus on the development of Location-Based Social Network (LBSN) for our micro-level housing appraisal modelling. A good location goes beyond the direct benefits from its neighbourhood. By leveraging housing data, neighbourhood data, regional economic cluster data and demographic data, we build a housing appraisal model, named HNED. Unlike most previous statistical and machine learning based housing appraisal research, which limit their investigations to neighbourhoods within 1km radius of the house, we expand the investigation beyond the local neighbourhood and to the whole metropolitan area, by introducing the connection to significant influential economic nodes, which we term Regional Economic Clusters. Specifically, we introduce regional economic clusters within the metropolitan range into the housing appraisal model, such as the connection to CBD, workplace, or the convenience and quality of big shopping malls and university clusters. When used with the gradient boosting algorithm 2 XGBoost to perform housing price appraisal, HNED reached 0.88 in R . In addition, we found that the feature vector from Regional Economic Clusters alone reached 0.63 in R2, significantly higher than all traditional features. Chapter 3 focuses on the exploration and validation of HNED modelling. In Chapter 4 and Chapter 5, we focus on macro-level housing price trend prediction. We fill the gap between the traditional macro-level housing market modelling and new developments of the concept of irrationality in microeconomic theories, by collecting and analysing economic behavioural data, such as real estate opinions in local newspaper articles, and people’s web searching behaviour as captured by Google Trend Index. In Chapter 4, we discuss the usage of micro-level behavioural data for understanding macro-level housing market behaviour. We use sentiment analysis to examine local newspaper articles discussing real estate at a suburb level in inner-west Sydney, Australia. We then calculate the media sentiment index by using two different methods, and compare them with each other and the housing price index. The use of media sentiment index can serve as a finer-grained guiding tool to facilitate decision-making for home buyers, investors, researchers and policy makers. In Chapter 5, we discuss how new developments of behavioural economic theory indicate that the information from decision-making at the micro-level will bring a new solution to the age-old problem of economic forecasting. It provides the theoretical link between irrationality and big data methods. Specifically, Google Trend Index is included as a new variable in a time series auto-regression model to forecast housing market cycles. To summarise the contributions of the thesis, we conclude that this is a successful early attempt to study housing price problems using data science methods, by leveraging newly available data sets and applying novel machine learning methods. Specifically, location-based social data improves the housing appraisal modelling. Human behaviour for housing market is analysed by introducing local newspaper articles and Google Trend Index into the modelling and analysis.

Concern about rising crime rates, high levels of unemployment and anti-social behaviour of youth gangs within particular urban neighbourhoods has reinvigorated public and community debate into just what makes a functional neighbourhood. The nub of the debate is whether concentrating disadvantaged people together doubly compounds their disadvantage and leads to 'problem neighbourhoods'. This debate has prompted interest by governments in Australia and internationally in 'social mix policies', to disperse the most disadvantaged members of neighbourhoods and create new communities with a blend of residents with a variety of income levels across different housing tenures (public and private rental, home ownership). What is less well acknowledged is that interest in social mix is by no means new, as the concept has informed new town planning policy in Australia, Britain and the US since the post Second World War years. Social Mix and the City offers a critical appraisal of different ways that the concept of ‘social mix’ has been constructed historically in urban planning and housing policy, including linking to 'social inclusion'. It investigates why social mix policies re-emerge as a popular policy tool at certain times. It also challenges the contemporary consensus in housing and urban planning policies that social mix is an optimum planning tool – in particular notions about middle class role modelling to integrate problematic residents into more 'acceptable' social behaviours. Importantly, it identifies whether social mix matters or has any real effect from the viewpoint of those affected by the policies – residents where policies have been implemented.