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The Journal of Real Estate Finance and Economics

Erschienen in:

23.07.2021

Medical Service Quality and Office Rent Premiums: Reputation Spillovers

verfasst von: Allen C. Goodman, Brent C Smith

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 3/2023

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Abstract

Location spillovers are a common theme in real estate and urban economics research, but this is the first test on the relationship between hospital service quality and the demand for proximate medical office space. We hypothesize that hospitals with reputations for high quality service represent an opportunity for physicians, and other service providers, to benefit from reputation spillovers. Further, the reputation benefit is capitalized into the practices’ willingness to pay for proximate office locations, thereby driving up the rental rates for nearby space. We find that distance from, and overall quality ranking of the hospital, both independent and in concert, are significantly linked to the base rents. The degradation in rent with distance is significantly greater when the hospital is ranked high in overall service quality, supporting the notion that a rent premium is linked to the high-quality hospital rather than simply an artifact of the neighborhood.

Vorheriger Artikel Tenant Concentration in REITs

Nächster Artikel Social Network Matters: Capital Structure Risk Control on REITs

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There are a number of professional reports that are produced by CBRE, Marcus & Millichap that focus on the MOB market, and most of these have only recently been offered.

At the time of this writing (February 2021), Covid-19’s impact on the real estate markets including the MOB is as yet uncertain.

We will often use the term “provider” rather than physician, because the modern health provider setting may offer nurses, nurse practitioners, technicians, laboratory personnel and support staff, all of whom provide health care services.

Not all physician specialists require proximity to the hospital to carry out their practices (e.g. dermatology, or general practitioner), so this directive is a generalization across all disciplines demanding MOB space.

The original dataset obtained from CoStar included approximately 19,500 observations. Roughly 615 proposed properties were removed, restricting the analysis to existing facilities. There were 1419 observations missing the year built that were removed. 199 did not have the number of stories listed, and 648 did not have the indication of base rent type (e.g. gross, net triple-net, modified). There were also 612 identified as mixed use (office with apartments or retail). These were eliminated in order to focus exclusively on the office market.

There is potential for bias in the CoStar data with respect to the represented sample identified as MOB properties. For example, many large health care systems have developed multi-function high quality facilities in affluent areas. These are typically leased exclusively to one health care system and will not be listed on CoStar. To the extent that they reflect proximity to desired location, their omission biases results against our findings. Additionally, some offices that house suppliers to the healthcare industry are listed in CoStar as MOB properties.

There is a valid case for creating distance bands based on travel time and we acknowledge the potential value in using such an approach. However, with over 70% of the entire observation set within 6000 m the difference between a travel and circular approach is likely trivial.

It should be noted that buildings proximate to a hospital will likely be attractive to medical professionals and health services support and thus compete in a premium rent market, even though they are not coded as medical in the CoStar database.

We have also tested individual quality indicators separately. They tend to move together, leading to multicollinearity issues. One might interpret the summary composite as similar (although not identical) to one derived through factor analysis of multiple attributes.

CoStar evaluates and rates properties using a five Star scale based on the characteristics of each property type, including: architectural attributes, structural and systems specifications, amenities, site and landscaping treatments, third party certifications, and detailed property type specifics.

This premium is due, in part to a higher cost per square foot for finish with more plumbing, electrical capacity and other base building enhancements (partitioning) frequently required in MOB space.

Goodman and Smith (2020) provide a detailed discussion of CON. The regulation (still active in 35 states) emerged from a belief that providers would pass along the higher capital costs to consumers and insurers in the form of higher prices.

In a test of small sample bias we dropped all those properties in counties with less than four observations and generated the same mean and premium measures. The premiums for the reduced sample are statistically identical to those obtained from the entire dataset.

It should be noted that the variable constate will be incorporated into the models interacting with medical with the variable name conmed.

The HLM approach is selected based on the documentation presented above and also on the fact that the chi-square test confirmed a statistically significant improvement in explanatory power over OLS. All results from the models that follow are available in OLS upon request.

The following models are available as ordinary least squares regressions on request.

Gross and triple net are compared to a combined variable representing net and modified.

As previously noted this is consistent with prior research (Goodman & Smith, 2020) that confirms there is a premium in medical specific space for properties located in a Certificate of Need State. The premium is an artifact of the distortions in space supply resulting from CON policies.

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Titel: Medical Service Quality and Office Rent Premiums: Reputation Spillovers
verfasst von: Allen C. Goodman
Brent C Smith
Publikationsdatum: 23.07.2021
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 3/2023
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-021-09855-z

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