Overview of behavioral real estate research findings

This section provides a broad and brief overview of some interesting behavioral findings in real estate.  If this section is of interest to you, we encourage you to read the papers cited at the end of this post.

First a little background.  Just about all research in the social sciences can be labeled “behavioral” to some degree.  Finance researchers examining stock price anomalies to real estate analyst studying housing prices are using transaction prices to represent a market consensus.  Ultimately markets consist of individuals with the human limitations on information problem solving.  Stock and real estate transaction prices are ultimately just artifacts of human interaction and behavior.

Behavioral real estate research recognizes the importance of human judgment in real estate decision-making and its impact on real estate activities and environments.  With this perspective, behavioral research embraces the psychology discipline including the theories of the researchers introduced to you in this chapter (Simon, Newell, Kahneman, and Tversky).  Behavioral real estate research has also adopted many of the experimental research tools and techniques used by Kahneman, Tversky, and others. As a result, ‘behavioral’ real estate research has come to identify a brand of research that focuses on human decision-making behaviors in a real estate context that often utilizes experimental research methods and techniques.

As a research paradigm, behavioral real estate research possesses a framework, known as the activities model, a guiding theory, referred to as the information processing theory of human problem solving, and effective research methods. This type of research is still relatively new as two decades is a brief time period in terms of a research paradigm.  Let’s generally review some of this research.

Early real property behavioral research examined the valuation (appraisal) processes of professionals.  Today the bulk of the investigative product remains in this area although lending activities and negotiation activities have also been studied.  There are at least three reasons why research into the valuation process has dominated the early stages of the behavioral research program.

·       Valuation processes substantially influence value formation in property markets characterized by a critical lack of transaction information (limited available data).

·      Appraisers are relatively easy targets for research purposes since they are a well-defined and accessible group with widely accepted normative models (for example, in the US we have a prescribed appraisal process).  These normative models that provide accepted definitions of what valuation processes ought to be also provides a platform to examine what valuation processes actually are.

·      Many early behaviorists were themselves appraisers giving them important advantages, from designing experiments to interpreting results, in conducting behavioral research of appraisers. 

Thus far, behavioral investigation has focused primarily on appraisers, examining issues of descriptive versus normative processes, comparable sales selection, sources of valuation bias, and agent-client impacts.  The gathered evidence supports the view of the appraiser as a decision-maker seeking problem solving efficiency and pursuing simplifying heuristics to overcome information processing limitations.  The use of these efficient processes can become unconsciously embedded in their problem solving strategy, or routinized, and their automatic employment may lead to biases. 

Some specific findings from this research include the following:

·      More experienced appraisers develop shortcuts and screening strategies compared to the structured and systematic approaches employed by novices.

·      Geographic familiarity appears to be an important determinant in appraiser behavior, as appraisers operating outside of their area of geographic expertise appear to be susceptible to potential anchors.  This is also useful information to consider when employing an appraiser.

·      Pending sales price knowledge appears to influence residential appraisers comparable sale selection.  Coincidently, US appraisal regulations (specifically the Uniform Standards of Professional Appraisal Practice (USPAP)) require appraisers to consider any pending offers.  Formal regulation could be enforcing a heuristic behavior (anchoring and adjustment).

·      Coercive client feedback appears to influence the appraiser’s role perception and over-sensitize the appraiser to potentially low value estimates.  Clients and subsequently appraisers are overly concerned with arriving at appraised values that are “too low” and this concern influences their decision-making processes.

·      Evidence from cross-cultural comparative studies indicates that a descriptive model of American appraisers may not be transferable to British and New Zealand valuers.

In addition to research in the valuation area, some studies have been completed in the area of real estate negotiation and lending.  Results in negotiation exercises and among banking underwriters lead to similar general conclusions found with appraisers.  Some of the observed behavior is consistent with an agent-client bias and supports the view that some heuristic behaviors may be the unconscious, routinized response to pervasive agent-client concerns.

The behavioral approach to real estate research has contributed to our understanding of expert behavior in real property decision-making.  This contribution suggests that behavioral research efforts in other areas such as investor and consumer behavior should be beneficial.  A blending of behavioral results and methods with more traditional approaches to real property research should benefit both positive and normative goals of the discipline.

Further reading

Diaz, Julian III. Behavioural Research in Appraisal and Some Perspectives on Implications for Practice.  RICS Foundation Research Review Series (www.rics-foundation.org), August 2002.

_____. Peering Into the Black Box: A Behavioral Approach to Real Property Research. Paper presented at the May 18-21, 2000 Session of the Weimer School of Advanced Studies in Real Estate and Land Economics, 2000. 

_____. Editorial.  Journal of Property Valuation and Investment 15:3, 1997, 222.

 

Heuristic behavior, bias, and anchoring and adjustment

Because of the limited amount of storage and processing capacity in short-term memory, humans develop simplifying cognitive shortcuts or production rules to solve complex problems. These simplifying production rules are com­monly known as cognitive heuristics or simply, heuristics. Although heuristic behavior is an efficient means of information processing, these mental short cuts can lead to systematic errors.  Systematic errors are errors that people make on a regular basis due to the limitations of human information processing.  Common mistakes in human information processing may result in judgmental bias, or simply ‘bias.’  Unsystematic (random) errors or mistakes may still exist, but it is important to emphasize that only systematic errors result in bias.

Recognizing the importance of Simon’s limitations on human information processing, Daniel Kahneman and Amos Tversky were interested in the effects of these limitations on human decision-making.  See Box 2 below for more information on Nobel Prize recipient Daniel Kahneman.  Through a series of relatively simple experiments, mainly with college students, Kahneman and Tversky discovered several types of cognitive heuristics that humans tend, more often than not, to use in problem solving. 

It is important to emphasize that the limitations on human information problem solving identified by Simon and Newell and the findings of Kahneman and Tversky apply to all humans and not just to real estate decision-makers in particular.  Heuristic behaviors have been documented in the problem solving strategies of a wide variety of problem solvers from gamblers to medical doctors.

Through the Kahneman and Tversky experiments, many varieties of heuristic behaviors in human decision-making have been identified.  A few of the initial heuristics discovered included representativeness, availability, and anchoring and adjustment.  In addition to these initial heuristic behaviors, others have been identified such as over-confidence, over-reaction, and sentiment.  Although humans employ many heuristics behaviors, the anchoring and adjustment heuristic has been ever-present in many problem-solving situations and we will concentrate on this heuristic rather than try to cover all heuristics types in one post.  

Generally, humans employ the anchoring and adjustment heuristic when faced with ill-defined problems requiring a numerical judgment.  With this type of problem, most people will use a strategy of starting from an initial value, called the anchor, and make adjustments from this initial reference point to arrive at final judgments. The systematic error occurs because we typically make insufficient adjustments to the anchor resulting in judgmental biases. 

Limitations on short-term memory: an example

As an example of the limitations of short-term memory, consider the following demonstration. Please time yourself and take 30-seconds (and only 30-seconds) to memorize as many of the following list of 15 eight-number sequences as you can: 

23984023

23948098

12093507

23339999

76035823

87030345

67009423

79843903

93495877

76098540

70348520

67094803

34095763

34957230

23457266

00004444

 

Now, please minimize your browser and write down as many of the number sequences as possible. Return to your browser when you have finished writing.

 

How did you do? 

Well, with few exceptions, you probably remembered 2 to 4.  Most people remember the sequences 23339999 and 00004444, but not many sequences beyond these two.  One of the sequences might have reminded you of a phone number that you have already committed to long-term memory.  On the rare occasion, someone can recall 10 to 15.  People who can commit the entire list to memory are said to have ‘photographic’ memories, but actually they have developed special techniques or sort-cuts to committee large amounts of information to memory.

Again, do not feel bad if you could only remember a few.  This is a demonstration to point out the human limitations of short-term memory and we are all human (blog author included!).  If you were given more time, you could continue to practice these number sequences and store the complete list in long-term memory.

“Gut feeling” in Real Estate Decision-making

If you ask a developer, investor, lender, or an appraiser how they make a decision to develop, invest, lend, or make a value judgment, you might get an involved, complex answer.  However, a common aspect of all these answers might be the final step which is typically, “the decision ultimately came down to a ‘gut feeling.’”  Gut feeling is an expression people use to explain the decision-making step, usually the final step before the decision is made, in which they have to use judgment to solve an ill-defined problem.   An ill-defined problem is a situation where the parameters of the problem are not clearly structured and there is subsequently no single correct or easy answer.  Another way to express the concept of an ill-defined problem is decision-making under uncertainty. 

The ‘gut-feeling’ concept is particularly important in real estate context because most real estate problems are ill defined and requires human judgment.  We will find that the real estate industry is complex and there is often limited data, which is usually incomplete with poor reliability, with no unifying theories for people to base objective decisions.  Therefore, subjective “gut feeling” is a substitute for an objective solution and subsequently human judgment is an important factor in a real estate decision (to develop, to invest, to lend, etc…).  To understand how real estate decisions are made, we first need a basic understanding of general human information problem solving.

Human problem solving model

Human information problem solving is the process humans go through to develop a solution to a proposed problem.  The fields of physiology and cognitive psychology have abundant research concerning human information processing. Two researchers in particular (Newell and Simon (1972) and Simon (1978)) developed a general 2-stage theory of human information problem solving.  

Simon’s 2-stage theory of human information processing recognizes that problem solving is the interaction between two features, the task environment and a human information processing system consisting of short-term and long-term memory. 

The task environment is the complex external environment in which the decision-maker operates.  The real estate industry is a prime example of a complex task environment.  Real estate decision-makers make judgments within the context of a complex economic, social, governmental, and environmental background.  Just consider the information that needs to be considered in a decision to develop vacant land: zoning, subdivision codes, interest rates, available capital, demographics, topography, drainage, construction costs, traffic counts, availability of utilities, access…to name a few!  Furthermore, real estate data is often unavailable, incomplete, and/or inaccurate, contributing to the complexity of this environment.

The human information processing system consists of two main features: short-term memory and long-term memory. All interaction between the task environment and the human information processing system is filtered through short-term memory, which makes short-term memory a critical component of this system.

Short-term memory functions as a filter because the task environment is complicated, continuous, and information rich, and short-term memory has limited storage capacity and relatively slow processing capability.  We will find that the storage and speed confines of short-term memory impose important limitations on human judgment.   Short-term memory is vital to human information processing not only as the link to the task environment but also because short-term memory is the brain feature and location where human problem solving actually takes place.

Physically, short-term memory is composed of two components, a language inter­preter and a problem space. The language interpreter’s function is to under­stand the problem.  The problem space, which maintains control over short-term memory, is the information processing system’s representation or model of the task environment and is responsible for the problem solving function.

The problem space (appropriately named!) causes the size and speed limitations attributed to short-term memory. First, problem space capacity is limited to approximately four to nine ‘chunks’ or pieces of information derived from either the language interpreter or long-term memory. Second, problem space processes information serially (one chunk at a time), which is a severe limitation on processing speed. These limitations are important characteristics of human information processing and the reason why we are all ‘only human’ and make ‘human mistakes.’ 

In contrast to short-term memory, long-term memory consists of large database like storage, called semantic memory, with an indexing system composed of recognition memory and asso­ciative structures. Semantic memory has unlimited storage capacity; however, the serial recognition memory indexing system is slow and tedious. Associative structures establish “smart” shortcuts to the semantic memory and quicker links to semantic information. 

In a paper published in 1978, Simon explained that this general 2-stage model of human information process­ing is robust for both novice and expert problem solvers who are solving both well-structured (or defined) and ill-structured (or undefined) problems. However, significant differences between novice and expert information processing do exist.  First, in short-term memory, experts form larger and richer data chunks, sometimes referred to as nested chunks, which expands the problem space’s processing capabilities.  Second, novices generally access semantic memory through the slow and serial recognition memory index. However, through experience and practice, experts develop quicker links to semantic memory with the associative structures providing efficient indexing, list structures, and intelligent links.  Novices, with little experience to draw upon, have not yet devel­oped the store of knowledge, both in terms of nested chunks and associative structures, needed to create more efficient “expert” indexing systems.