Empirical Investigation into the Limitations of the Normative Paired Sales Adjustment Method

This study investigates the normative paired sales adjustment method employed by appraisers in the sales comparison approach. It finds that the method fails to account for the diminishing marginal price effects of property attributes. The study develops an empirical model to test the marginal price effects of view and lot-size amenities. The finding is that the empirical data confirm land economic theory and identify a need to study and develop improved methods for estimating adjustments to comparable sales.

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

Trading constraints and the investment value of real estate investment trusts: an empirical examination

This study focuses on the property-derived cash flows that a REIT investor earns. We observe that, in the short run, REIT investors are only exposed to the income cash flows of a REIT's underlying portfolio and not to its property price fluctuations. Specifically, investors miss out on the component of appreciation returns not contained in income. Chapter 3 observes this phenomenon and argues, without proof, that this is due to the trading restrictions that REITs face in order to operate tax free, which impose minimum holding periods on properties in REITs' portfolios. Chapters 4 and 5 show that the trading-restrictions explanation is indeed the reason for this phenomenon. Specifically, chapter 4 tests how REITs with different firm characteristics are differently affected by the trading constraints. Firstly, we test for size effects and find that medium-sized and large firms offer investors better exposure to short-term fluctuations in property appreciation than small firms. This supports the trading restrictions hypothesis, as large firms are less affected by these. Secondly, we test for the effects of the degree of diversification in a REIT's portfolio and find that, while investing in a REIT which is diversified by property type gives an investor better exposure to appreciation cash flows, investing in one whose portfolio is merely geographically diversified does not. Finally, we test whether UPREITs give an investor better exposure to property appreciation cash flows and find strongly that this is so. Since the partnership that holds the property in an UPREIT is not subject to selling constraints, we find our hypothesis strongly supported. Chapter 5 analyzes holding periods and selling decisions. We firstly simulate a possible filter-based market timing strategy which significantly outperforms a simple buy-and-hold strategy, and demonstrate to what extent holding periods shorter than what is allowed are required. We then analyze actual holding periods of properties in REITs' portfolios and model the decision to hold a property beyond four years, finding strong evidence that there is an incentive to do so in a rising market. This gives strong support to the trading-restrictions explanation

Applying Models for Vertical Inequity in the Property Tax to a Non-Market Value State

The objective is to contribute to the discussion on property tax inequity by employing the methodologies developed to test for vertical inequity in a tax system that currently does not rely on some form of market value in the assessment process. There is strong evidence that the property tax and the ‘‘True Tax Value’’ assessment procedure employed in Indiana contains progressive vertical inequities rather than regressive inequities as is typically perceived. This is unique, as previous findings tend to support the notion that the property tax is regressive. It provides potentially pertinent information in light of the ongoing discussion surrounding the restructuring of Indiana’s property tax assessment and property tax debates elsewhere.

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

Bargaining over Residential Real Estate: Evidence from England

This paper presents and investigates a new data set of individual residential property transactions in England. The main novelty of the data is the record of all listing price changes and all offers made on a property, as well as all the visits by potential buyers for a subset of the properties. We analyze individual seller and potential buyers behavior within property transaction histories. This leads us to establish a number of stylized facts pertaining specifically to the timing and terms of agreement in housing transactions, and more generally, to the sequence of events that occur from initial listing to sale agreement. We assess the limitations of existing theories in explaining the data and propose an alternative theoretical framework for the study of the strategic interactions between buyers and sellers that is consistent with the empirical evidence.

Sustainable Real Estate: Management, Assessment and Innovations

Production and consumption activities have determined a weakness of the sustainable real estate economy. The main problems are the subordination of public decision making, which is subjected to pressure from big companies; inefficient appraisal procedures; excessive use of financial leverage in investment projects; the atypical nature of markets; income positions in urban transformations; and the financialization of real estate markets, with widespread negative effects. A delicate role in these complex problems is assigned to real estate appraisal activities, called to make value judgments on real estate goods and investment projects, the prices of which are often formed in atypical real estate markets, giving ever greater importance to sustainable development and transformation issues. This Special Issue is dedicated to developing and disseminating knowledge and innovations related to most recent real estate evaluation methodologies applied in the fields of architecture and civil, building, environmental, and territorial engineering. Suitable works include studies on econometric models, sustainable building management, building costs, risk management and real estate appraisal, mass appraisal methods applied to real estate properties, urban and land economics, transport economics, the application of economics and financial techniques to real estate markets, the economic valuation of real estate investment projects, the economic effects of building transformations or projects on the environment, and sustainable real estate

"Juntos pero no revueltos" : the influence of the social stratification system on urban densification patterns in Bogotá, Colombia

From the 1950s to the 1970s, Bogotá, Colombia was one of the fastest growing cities in the world. During this period, the city became characterized by extreme social and geographic polarization between rural migrants and urban elites. This polarization was caused by a lack of development control as well as planning policies that encouraged social and spatial segregation. Social elites primarily lived in suburban neighborhoods in the north of Bogotá, which were well served by municipal infrastructure and enjoying easy access to services and employment opportunities. Low-skilled rural migrants settled in informal neighborhoods in the south that had poor municipal services and were close to environmentally polluted areas and far from the central business districts. Faced with the prospect of continuing, ungovernable urban sprawl led by both the formal and informal sector, in 1979 the city implemented a set of growth control and densification policies. However, thirty-five years later these policies have failed to halt or reverse the uneven development of the city. I argue that the unintended outcomes of the growth management policies are largely due to private sector interests and actions, which in turn vi are influenced by social equity policies. To demonstrate this, I correlated the recent densification projects with the so-called Stratification system. This system separates the city into six levels based on built form characteristics to identify groups with different income levels, providing a proxy for the analysis of socio-spatial segregation patterns. In addition, I explored the behaviors and attitudes of urban development agents through interviews and analysis of planning documents. I found that there is a statistically significant correlation between the Stratification zones and the densification patterns shaped, in part, by the influence of the private sector over local land-use and density regulations. This influence of developers has led to a transformation of the built form that is distinguished by uneven density levels, access to services and employment, and concentrations of poverty. Because of this complex articulation of planning and social policies with private sector interests and actions, Bogotá’s low income residents are experiencing unpredictable patterns of disinvestment, overcrowding, revitalization or dislocation in their neighborhoods.Community and Regional Plannin

Towards a Pan-European property index : methodological opportunities

Thesis (S.M. in Real Estate Development)--Massachusetts Institute of Technology, Dept. of Architecture, Center for Real Estate, 2004 [first author]; and, (S.M. in Real Estate Development)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, Center for Real Estate, 2004 [second author].This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 85-87).This study examines the methodological opportunities of index construction for the Pan-European property index, whose release is planned by the company Investment Property Databank (IPD). To address the question of temporal aggregation in appraisal indices, three index construction methods, namely "Stale Appraisal", "Linear Interpolation", and "Repeated Measures Regression", are tested for their accuracy in dealing with infrequent appraisals. Our model is based on a simulation approach, calculating appraised indices from a simulated "true index" of randomly generated returns, and directly comparing the statistical characteristics of these index returns to the true return. As broader context, this paper also gives an overview of the current theories in respect to general valuation issues on a disaggregate, aggregate and international level. We also investigate the European real estate market regarding currently applied market size measuring, structure and country performance. In particular, we explore crucial valuation issues that are relevant for the planned Pan-European property index to obtain the respect of the international investment community.by Friederike Helfer and Markus Witta.S.M.in Real Estate Developmen