Research Productivity in the Top 3 Core Business Real Estate Journals

The attached spreadsheet reveals the top research producers in terms of scholarship creation for the last five years in the leading Core Business real estate journals. The list is designed to serve as a tool for public policy makers (predominately state and federal government legislators and their staffs), industry practitioners, and trade associations to assist them in their placement of sponsored research. Additionally, the list should prove most helpful to College of Business Deans and University Presidents seeking to expand their school’s and/or university’s focus into the field of real estate.

(View and download the attachment.)

In no certain order of importance, the Top 3 Core Business real estate journals are defined as REE (Real Estate Economics), JREFE (Journal of Real Estate Finance and Economics) and JRER (Journal of Real Estate Research). While these journals cover a broad list of topics, they can be said to focus on the business side of real estate covering topics such as market liquidity, property pricing, duration analysis, likelihood of a transaction, mortgage markets, income-producing property, REITs, and buyer versus renter markets, among other issues. [i]

The list concentrates on the concept of scholarship creation. Where the creation of scholarship is defined as the process of developing a research question, testing this question, submitting the work for blind peer review, and the acceptance of this work by a scientific academic journal. Thus, publication acceptance is a necessary and sufficient condition for an author being credited with the creation of scholarship. For each manuscript, each author associated with that particular work is credited with the production of one piece of scholarship. This is not the usual convention in articles that seek to rank authors and universities research productivity. Typically, the convention is to rank by metrics such as page counts and weighted shares. However, this list is not designed to be a ranking tool. Instead, the list is designed as a tool that is easily decipherable and useful to the parties outlined above. [ii]

Articles in the Top 3 Core Business real estate journals published between January 1, 2007 and December 31, 2011 plus forthcoming articles accepted by 12-15-2011 are itemized in the list.[iii] Tentatively, the list will be updated each year during the month of December. For example, the list should next be updated for articles published between January 1, 2008 and December 31, 2012 plus forthcoming accepted papers appearing on the three Core Business real estate journals’ websites on or before 12-15-2012. In this way, the list constantly reflects current research productivity. Additionally, the list contains other information such as manuscript titles to assist list readers in determining the area or areas of expertise of individual researchers.

If unaltered, the list in spreadsheet form may be copied and redistributed without restrictions. Other questions including (but not limited to) possible corrections can be directed to:

Ken H. Johnson
kenh.johnson@fiu.edu

ENDNOTES


[i] Urban Econ journals are not covered in this list. While there is considerable overlap between Core Business and Urban journals, Urban Econ journals tend to concentrate in areas such as urban growth models, the social benefits of ownership, and agglomeration.
[ii] There is, of course, enough information within the provided spreadsheets for any interested party to produce the traditional ranking metrics. In fact, we encourage such work as it will serve as additional information on the most current producers of Core Business real estate research.
[iii] Forthcoming articles are those articles that have been accepted for publication but that are not yet in print. The criteria employed for the list requires that the piece be listed as forthcoming on the three journals’ websites by 12-15-2011.

© Copyright Ken H. Johnson. This material may be freely duplicated and republished under the following conditions: (a) the author’s name must be clearly visible; (b) the author’s journal affiliation must be clearly visible; and (c) the author’s university affiliation must be clearly visible. Otherwise, this material may not be reproduced in any form without the written consent of the author.

Practical Interpretation of Real Estate Research

This blog is devoted to practical interpretations of present and past real estate research.  The idea is to “boil down” research findings so that they may be applied by those in the field and employed by consumers of real estate services.  Thus, the readership is expected to be mostly practitioners (real estate professionals, lenders, developers, appraisers, etc.) and the general public interested in real estate.

The primary, though not exclusive, author will be Ken H. Johnson.  As Editor of the Journal of Housing Research (JHR) and a former practitioner, Dr. Johnson has a unique understanding of both research and practice. Many of the condensed pieces are reviews from original works in (or forthcoming in) JHR.  Others are reviews from additional leading real estate research outlets: Real Estate Economics (REE), Journal of Real Estate Finance and Economics (JREFE), Journal of Real Estate Research (JRER), Journal of Urban Economics (JUE), and Journal of Housing Economics (JHE), among others.  Some few are thoughts and ideas for working papers or simply editorials.  All articles contain some new analysis beyond the scope of the original piece.  Most often, this new analysis is simply an outline of how research findings might be employed in practice.  At other times, these “boiled down” articles might take opposing viewpoints.  Regardless, the idea is to create an aware and thinking market place.

In an academic sense, these “boiled down” pieces can be thought of as single citation literature reviews.  In an applied sense, these short pieces can be viewed as an attempt to provide market practitioners and consumers with the latest research findings in a fashion so as to make them more useable to both practitioners and the general public.

All readers should understand that all real estate research is potentially flawed.  Just as with medical research, findings are not guaranteed. Instead, findings provide insight, not foresight.  Thus, the ultimate goal here is to provide the latest information to practitioners and consumers of real estate services so as to increase awareness.  In short, informed practitioners and consumers should lead to more transparent and healthier real estate markets.

Also, see http://realestate.fiu.edu/.

Buyer or Renter Nation?

A Little More on Wealth Accumulation and Homeownership

Last week’s posting (Homeownership Leads to Greater Wealth Accumulation: But How?) concentrated on how homeowners, on average, accumulate more wealth than renters. The gist of the posting was that in an environment where most do not save homeownership creates a “forced piggybank” for owners through amortization of their mortgages and prepayment of principal. This conclusion comes from ongoing research being conducted by Beracha and Johnson. [i] Additional evidence indicating that homeownership is really a savings vehicle is provided when Beracha and Johnson’s Buy vs. Rent model[ii] is employed to estimate the probability that renting is the superior economic decision and leads to greater wealth.[iii] In particular, Beracha and Johnson balance the benefits of ownership against the costs of homeownership and compare wealth accumulation through home equity against wealth accumulation through investments created by a comparable renter. [iv] The model assumes an eight year holding period. [v]

Through thousands of Monte Carlo simulations (an advanced statistical technique where past outcomes are used to predict future results), Beracha and Johnson are able to derive the probability that renting will outperform homeownership in terms of wealth accumulation for each of the 30 plus years of the study. When the probability for a given year is 50%, there is no clear winner between ownership and renting. When the probability is greater than 50%, renting wins. When the probability is less than 50%, ownership wins. The results are presented in the graph below:

Probability That Renting is Preferred to Buying - graph

The top line in RED depicts the probability that renting will outperform ownership in wealth accumulation assuming that renters reinvest rent savings (difference between rent payments and mortgage payments). Clearly, over the vast majority of the study period and under this very strict assumption of reinvestment of rent savings renting provides the greater probability of wealth accumulation.

The bottom line in BLUE, however, depicts a different and perhaps more realistic outcome. In particular, when renters are not forced to save and reinvest their rent savings and are instead allowed to spend on consumption, ownership becomes the probability winner in all of the wealth accumulation races.

Implications

Evidence is continuing to mounting that renting is a better path to wealth accumulation in a strict “horserace” between buying and renting where renters are forced to reinvest any rent savings. Therefore, for those that have the discipline to monastically reinvest rent savings, renting is probably the better path to wealth accumulation. However, in perhaps a more realistic setting where renters can spend on consumption (beer, cookies, education, healthcare, etc.), ownership is the clear winner in wealth accumulation. Said another way, homeownership is a self-imposed savings plan on the part of those that choose to own.

ENDNOTES


[i]Eli Beracha and Ken H. Johnson, 2012, Beer and Cookies Impact on Homeowners’ Wealth Accumulation, ongoing research.
[ii]Eli Beracha and Ken H. Johnson, 2012, Lessons from Over 30 Years of Buy Versus Rent Decisions: Is the American Dream Always Wise? Forthcoming in Real Estate Economics.
[iii]The model results simply need to be inverted in order to interpret the results as to when buying leads to greater wealth accumulation.
[iv]See Beracha and Johnson (2012) for exacting details of their Buy vs. Rent model.
[v]The holding period can be varied with little change to the results. This issue will be addressed in future postings.

© Copyright Ken H. Johnson. This material may be freely duplicated and republished under the following conditions: (a) the author’s name must be clearly visible; (b) the author’s journal affiliation must be clearly visible; and (c) the author’s university affiliation must be clearly visible. Otherwise, this material may not be reproduced in any form without the written consent of the author.

Homeownership Leads to Greater Wealth Accumulation: But How?

Several real estate economists have shown that the average homeowner accumulates more overall wealth than the average renter. [i] However, it is not clear how this is done. Is it that owned property usually appreciates at such a rate that after considering leverage returns to ownership are extraordinarily high? Said another way, might homeowners accumulate more overall wealth because ownership is a great levered equity creator through property appreciation? Or, is it that owners acquire greater wealth, on average, because they are systematically paying down a mortgage thereby creating equity thanks to loan amortization? In other words, paying off property creates wealth.

In ongoing research being conducted by Beracha and Johnson,[ii] these and other questions concerning homeownership and the accumulation of wealth are being investigated. In earlier research, Beracha and Johnson show that renting is the superior investment strategy; however, in this earlier strict horserace between buying and renting, a very bold assumption is made. Specifically, it is assumed that any rent savings (from lower rent versus mortgage payments) are reinvested without fail and after balancing all of the costs and benefits from ownership and comparing them to renters’ portfolios from reinvesting rent savings, renting wins.

The question, however, very quickly becomes that in a setting where Americans generally save less than 5% of their disposable income, is this assumption realistic and how might the removal of this reinvestment decision alter the outcome of the horserace between buying and renting? As part of their current research, this question is directly addressed. In particular, Beracha and Johnson find that after allowing renters to spend any rent savings on consumption (beer, cookies, healthcare, education, etc.), ownership leads to greater wealth accumulation, on average. The graph below highlights this finding.

Renters' Portfolio Values Divided by Owners' Sales Proceeds - graph

The graph looks at the ratio of renters’ portfolio values to owners’ proceeds from sale for the entire U.S. between 1978 and 2010 both with strict reinvestment of rent savings and without reinvestment of rent savings.[iii] Clearly, numbers greater than 1 indicate that renting leads to greater wealth accumulations, while numbers less than 1 indicate that homeownership creates greater wealth, on average.

When renters are forced to reinvest (top line in the graph), the results confirm the earlier findings of Beracha and Johnson (2012). That is, in a strict horserace between buying and renting, renting wins in the vast majority of cases. However, when renters are allowed to spend rent savings on consumption (i.e. economically act like the typical American consumer), homeownership wins in virtually all instances. Notice that in the bottom line of the graph (no reinvestment), the renters’ portfolio values divided by owners’ sale proceeds is greater than 1 for only four of the 32 years of the study. Thus, when renters are allowed to spend rent savings, homeownership is the clear winner in the wealth accumulation horserace.

Finally, in the same current research, Beracha and Johnson find that allowing for property appreciation rates to increase as much as 20% over their actual historic values results in virtually no change in the outcomes concerning wealth accumulation. That is, property appreciation contributes only marginally to wealth accumulation.

Implications

Without proof many have speculated about this outcome for years. However, there is now actual quantifiable evidence that homeownership is not the great levered equity creator that it has so often been touted to be. Instead, it appears that homeownership creates extra wealth mainly through its ability to force owners to save rather than through property appreciation. Thus, homeownership appears to be a self-imposed saving, which through time leads to greater wealth accumulation as compared to comparable renters. In short, buying a home makes Americans save.

Who says that Americans are horrible savers? Apparently, we are not. We have simply been saving through our homes rather than putting our savings in the bank.

ENDNOTES


[i] Homeownership is the most viable path to wealth creation for the majority of Americans. See Engelhardt (1994), Haurin, Hendershott and Wachter (1996), and Rohe, Van Zandt and McCarhty (2002), among others.
[ii] Eli Beracha and Ken H. Johnson, 2012, Beer and Cookies Impact on Homeowners’ Wealth Accumulation, ongoing research.
[iii] The research assumes 8-year holding periods. When the holding period is allowed to vary between four and twelve years, the results change only marginally. Thus, holding period has very little to do with the results.

© Copyright Ken H. Johnson. This material may be freely duplicated and republished under the following conditions: (a) the author’s name must be clearly visible; (b) the author’s journal affiliation must be clearly visible; and (c) the author’s university affiliation must be clearly visible. Otherwise, this material may not be reproduced in any form without the written consent of the author..

The Evidence is in on the Choice of a Lockbox

Does the choice of a lockbox matter? Do the older type lockbox systems influence the final transaction price or the marketing time of property? These questions are often pondered by real estate professionals. Older key and combination systems are low tech, easy to employ, and less costly to the broker. Newer electronic lockboxes are often more complicated, provide additional information by way of technology, and are slightly more expensive than their low tech counterparts. The trade-off is therefore between ease of use, information, and cost of operation.

If the different lockbox systems do not influence transaction outcomes (price and marketing time), then the choice of the lockbox system can be left up to the broker without costs to the sellers of property. On the other hand, if one system produces either a pricing discount or extended marketing times, then brokers need to be aware of these differences in order to better serve their clients.

Recent research by Benefield and Morgan answer these questions.[i]. The researchers directly test for the impact of lockbox type (newer electronic versus older systems) on property price and property marketing time. After controlling for other difference in listings such as location, age, size, seller motivation, and quality, Benefield and Morgan find that older lockbox systems, on average, do not influence the time it takes to market property. Property pricing, however, is another matter. Specifically, Benefield and Morgan find a negative impact on price from the use of the older lockbox system. More to the point, older lockbox systems appear to not influence marketing time but result in lower selling prices. The pricing discount was a staggering seven percent on average.[ii].

IMPLICATIONS
There is now statistical evidence (not just professional speculation) that indicates the inferiority of the older lockbox systems. Therefore, wherever financially practical, brokers should stop their use of older key and combination lockbox systems in favor of the newer electronic systems. It now appears that these newer electronic lockboxes lead to a better sharing of information and feedback between listing and showing brokers resulting in better prices.

ENDNOTES:


[i]. Benefield, J. D. and J. M. Morgan, Ease-of-Access, Home Prices, and Marketing Times: The Choice of Lockbox Type, Forthcoming in the Journal of Housing Research.

[ii]. The authors believe that at least part of this discount is related to the type (mostly lower priced, lower demand) properties on which the older systems are employed.

© Copyright Ken H. Johnson. This material may be freely duplicated and republished under the following conditions: (a) the author’s name must be clearly visible; (b) the author’s journal affiliation must be clearly visible; and (c) the author’s university affiliation must be clearly visible. Otherwise, this material may not be reproduced in any form without the written consent of the author.

A Simple Measure of Market Liquidity is a Better Indicator of Market Health than Pricing

What is the definition of a healthy housing market? Is it a housing market in which home prices are decreasing? Few would agree with this. Is it a market in which home prices are increasing? At first glance, many would agree with this definition. However, increasing prices cannot be used to diagnose a healthy housing market. If increasing prices indicate market health, then in 2005 housing markets were “very” healthy, and we know that this is not true.

If pricing does not indicate market health, then what does? The answer is simple, it is market liquidity and not pricing that indicates the health of a housing market. Liquidity has been defined in many ways but it basically boils down to: can an individual seller, at a time of their choosing, successfully market their property at or near market value? We often hear of rates (turn-over and absorption) that are related to this concept. Unfortunately, these measures are difficult to estimate and they all have something to do with outstanding inventory. What really matters, regardless of outstanding inventory, is the likelihood that a property will close. This is the most basic meaning of market liquidity and it can easily be proxied.

All of the data necessary to proxy a particular market’s liquidity (and thereby its health) is available on the daily “hot sheets” of almost every MLS in the country. Since liquidity is really just a batting average, all that needs to be done is total the successful transactions (closed properties) and divide these by the failed listing transactions (Expireds + Withdrawns + Cease Efforts + Cancelled)[i] [ii]. The resulting number is a very close approximate to the probability that any given property listed in that market will close and an increasing trend in this number indicates improving market health.

CONCLUSION

Pricing trends do not indicate the health of a housing market. Keep in mind. For almost every sell in an increasing market, there is a repurchase at a higher price. For almost every sell in a decreasing market, there is a repurchase at a lower price. Thus, pricing is a “double edged sword”. Gains/Losses on a sell are almost always accompanied by higher/lower repurchases. Thus, pricing trends can never indicate the health of a particular real estate market. Instead, it is market liquidly, which can be easily proxied, that actually indicates market health. After all, the real goal is for a seller of property to be able to transact at or near market value with a high degree of certainty. Fortunately, most MLS’s around the country have the information at their fingertips to estimate the health of their particular market.

It is liquidity (not price) that matters.

ENDNOTES


[i]. Different MLS’s have similar but not exact designations for these various categories. The goal is simply to divide successes by failures.
[ii]. The timing of the calculation will depend on the number of outcomes each day on a particular market’s MLS hot sheet. The goal is to avoid a mathematically undefined estimate. Thus, larger markets might do this average daily, while smaller markets might only calculate this average on a monthly basis. If interested, any MLS needing assistance in setting up this estimate may contact me.

© Copyright Ken H. Johnson. This material may be freely duplicated and republished under the following conditions: (a) the author’s name must be clearly visible; (b) the author’s journal affiliation must be clearly visible; and (c) the author’s university affiliation must be clearly visible. Otherwise, this material may not be reproduced in any form without the written consent of the author..

The Costs of Flood Zone Uncertainty

The cost, in terms of final transaction price, of being in a flood zone is established. Specifically, it appears that being in a flood zone reduces the final transaction price of a property between 4.1% and 4.3% on average [i], [ii]. These estimates assume flood zone status is clearly denoted in the MLS. However, what happens when the flood zone status of a property is unclear during the marketing process of a property, which is very often the case as flood zone mapping, is notoriously inaccurate? Are there any indirect costs such as a lower likelihood that the property will close? Concurrently, does flood zone uncertainty create a lower probability of a broker earning a commission?

This question is answered in Chang, Dandapani, and Johnson (2010)[iii]. In this study, the authors investigate for a lower chance of a closing due to uncertainty over a listing being located in the 100-year flood zone. In this particular study, properties in the MLS where listed as either: (a) property is definitely in the 100-year flood zone, (b) property is definitely not in the 100-year flood zone or (c) it is uncertain if the listed property is in the 100-year flood zone.

The study finds that properties listed as uncertain if they are in the 100-year flood zone are, on average, marketed 1.22 times before a closing actually occurs. Thus, uncertainty over flood zone status in the MLS leads to a 22% greater likelihood that the seller will not close their property during a given typical listing period. This, of course, directly translates into a 22% greater chance that the listing broker will not earn a commission or, in the case of a relisting, the listing broker will have to work 22% more to earn the same commission.

Implications

In general, the results from Chang, Dandapani, and Johnson (2010) indicate that ambiguity in information entered into an MLS is not harmless. Thus, great care needs to be taken over the accuracy of MLS data entry. Basically, uncertainty is costly to both the listing broker and the seller of property. In particular, the results strongly suggest the need for an updating of the flood maps currently employed to determine flood zone status as clearer and more easy-to-understand maps expedite the selling process, thereby reducing the costs to sellers and brokers alike.

ENDNOTES


[i]. Harrison, D.M., G.T. Smersh, and A.L. Schwartz, Environmental Determinants of Housing Prices: The Impact of Flood Zone Status, Journal of Real Estate Research, 2001, 21, 3-20.

[ii]. Guttery, R.S., S.L. Poe, and C.F. Sirmans, An Empirical Investigations of Federal Wetlands Regulation and Flood Delineation: Implications for Residential Property Owners, Journal of Real Estate Research, 2004, 26, 299-315.

[iii]. Chang, C.H., D. Dandapani, and K.H. Johnson, Flood Zone Uncertainty and the Likelihood of Marketing Success, Journal of Housing Research, 2010, 19:2, 171 – 184.

© Copyright Ken H. Johnson. This material may be freely duplicated and republished under the following conditions: (a) the author’s name must be clearly visible; (b) the author’s journal affiliation must be clearly visible; and (c) the author’s university affiliation must be clearly visible. Otherwise, this material may not be reproduced in any form without the written consent of the author.