Property Location: Collateral or Condition?
Location big data helps real estate owner, creditor and investor see the real estate market in its entirety. Effective business profile of the property, or its ´best use’, is a function (derivative) of the commercial area around the property, that can be identified digitally for all locations across country and the globe.
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Location is very dynamic and becoming more and more so: it is not just about COVID-19 and similar cataclysmic events. It is mostly market change, the shift of people’s values regarding consumption, housing and transportation, work and leisure.
This constitutes a serious problem for property owners and those financial institutions that risk their capital in real estate. According to Moody’s Analytics, about 3/4 of commercial real estate price variations are directly related to the location of the property.
Architects and developers coined the wording “If we build it will they come?”, reflecting upon a hyper-dynamic change in consumer values and a serious mismatch between capital long-term nature of real estate development and fast change of location around property.
Professionals in Finance treat this type of problem using traditional financial ratios, such as Loan-to-Value ratio.
In the LTV formula, LTV = Loan/Value, the “condition” affects the risk on loan, and the “collateral” is associated with the value of the property. In other words, LTV = Condition/Collateral.
Condition is normally about the uncontrolled external environment, mostly “macro”, but local also inclusive. One way or another, it determines the appropriate risk-adjusted loan volume.
In the underwriting process “local” part of “condition” has always been subjugated to “macro” (inflation, etc). There are several reasons for that, such as local data being poorer than the national one, the local environment being more exceptional and unique, plus location analysis and underwriting being less standardized and more labor intensive.
On the contrary, location, as some may believe, must be reflected in the market value of the property, thus being a denominator in the LTV ratio.
Indeed some may treat location as an ingredient of collateral, not condition, as any real estate object by definition seems “glued” to its surrounding area, which may change over time, but hardly disappear. Location then appears as a sort of dowry to the property.
There is a strong rationale for treating “location as a collateral”: it is fixed around the property and its hierarchical position in the settlement system, relative to other locations, is normally pretty sustainable.
The only problem, as was mentioned above, would be the absence of the owner’s control over the location, and therefore the absence of the legal ground for pledging location, say, as a pool of stocks. But, the good news is that it does not belong to anybody else, so if we find a proper parameter of location performance, then we could make it an index, a financial benchmark.
Location as collateral
If we do know how to evoke financial value from a property location then we should be able to capitalize on it.
For example, one can try to pledge location (not property) as collateral in order to leverage money. The key challenge though: can we leverage something that we do not own, or control, and that we cannot properly measure?
Mortgage assumes that the owner of a property does exist and is responsible for the property and the debt payment, but who “owns” the “location of a property”? Obviously no one, or everyone. Then how to leverage it?
Location value is a part of property market value, so the owner should be able to “sell” and “pledge” location as a “part” of the property.
One way to do it is to sell or pledge property itself with the belief that somehow property location value will be reflected in the ultimate market price of the property.
This belief has solid ground as we know from the market experience that property prices vary a lot because of the location. But if so, why do we see, for example, Broadway in Los Angeles full of ArtDeco historical buildings either abandoned or poorly maintained? What stops their owners from unleashing the location potential of those buildings besides zoning regulations?
They either wait for zoning to be removed or cannot find enough capital to reconstruct old culturally meaningful buildings, demolish them (in case it is allowed by zoning) and build a new property.
Reconstruction costs may be too high and too risky for the capital market to accept vis-a-vis lengthy (re)construction period without revenue, and expected, but not yet real revenue after the reconstruction.
Property owners normally try to borrow money against the property per se. But it is a commingled asset, a blend of brick and mortar with location potential.
In this context, without recognition of location input, the property taken off the local context may and do diminish commercial potential created by its location in the eyes of investors, and, therefore, hinders the ability of the property owner to leverage money in the capital market by simply offering property location potential (future flows) as collateral.
Location has always been considered in every real estate transaction, but often severely discounted if no revenue could be attached to the property during the construction phase, or even later, as there is no material proof of it at the start of (re)construction.
Also, one may say, that influence of the location on property value has always been determined by market price, but it is only true for very fragmented localized real estate markets defined by very well-known immediate vicinities of the property. And what about larger market?
Indeed, there has been no such thing as a national real estate market yet, or global real estate market. So the prices have been established without proper property comparisons with ALL existing properties across the nation, or worldwide.
One could not see the real estate market in its entirety before big data made it possible, but now they can. Granular location data is doing this job for the real estate owner, but beyond that, it makes the real estate market visible in its entirety.
Now property owners know their relative standing vis-a-vis every location in the country and the globe. Data breaks the silo and lets owners, creditors, and investors understand and measure the amount of lost and undiscovered opportunities in the national/global market by simply comparing every location among them.
Because of that property portfolios have to be adjusted, new investment decisions have to be made. Place is no longer a safe haven. Each of the real estate owners, creditors and investors are on the spot now even if some still deny it.
Moreover, due to progress in spatial accessibility, most of the factors influencing property value now are far beyond the immediate reach. No matter how well property owners in Manhattan know their premises and the premises of their competitors, the power of all the Manhattan-based commercial real estate value lies in NJ, upstate NY, Pennsylvania, or Connecticut, 50 or more miles away from Manhattan.
It is not only about commuting: there are other factors such as zoning change, easiness of construction permits, new commercial attraction points, or new depressed areas that affect the demand for Manhattan-based property. Accessibility and fast change in the area development run the show now.
Securitizing location
Securitization is not an unusual instrument to improve credit quality.
Take banks or private companies as an example. What do they do if they need to restructure an unsuccessful part of their business? They leverage a successful part of the business through a securitization mechanism: decouple revenue streams generated by the successful segment of the business from the unsuccessful one.
Why don’t we try the same mechanism with commercial real estate property? The good thing about it is that bad properties can often be found in good locations, but good properties are rarely found in bad locations.
So, our target market is a “difficult” property (the bad part of the business) in a good location (the good part of the business). We know it is a commingled asset, and we need to split this amalgamated asset into property per se and its location. But how?
The key difference here with the classic securitization is about current and future revenues. Location can only generate revenue through the property, but if the property does not work yet (construction period), what would we do? Can we pledge future revenue from the location in case we would like to tap capital and financial markets?
Futures, promissory notes, project finance, tax increment bonds and many other instruments of this sort — pledging/promising future revenues — do exist. They count on well-known and standardized commodities (oil, for example), or revenue-generating facilities/objects (toll roads, hospitals, etc). Real estate locations may also be part of this list.
If we want this to happen, we need to find a standard way of defining location and measuring locations in monetary terms.
So how do we define location?
Every location is function (use)-specific. There is no abstract location. It can work for one function and not work for another. It can work for many functions or for just a single one. If we want to measure a location’s pledge power for future revenues, we, first, need to define with what particular function would they most probably be achieving, and what are the best use options for our property, supported by location.
There is a data-driven way to do that, so each current vs. best use mismatch becomes measurable in revenue terms. Let us explore this further.
First, we differentiate “property” from its “location”. Location does not mean just a lat/long position of the property, it is not a dot. It is an area around the property that creates a demand for the services housed inside the property.
Such areas are often called trade areas, or catchment areas. Their boundaries may be defined in different ways: by distance, travel time, or visitor origins. Our current methodology focuses on travel time (accessible area within a given time period by foot, car or public transport), as time is more telling than distance, considering traffic and congestion.
One hour of travel time by car can define an area within a 50-mile radius around the property. The commercial success of this property can equally depend on the close proximity and relatively remote areas. Conventional real estate underwriting normally looks at the immediate vicinity, but we look at full accessibility space. The fate of an office building in Midtown Manhattan may be determined in Philadelphia (NJ), Hartford (CT), or Pike County (PA).
The property’s commercial profile is highly sensitive to the time people or commodities travel and to the mode of transportation.
As transportation infrastructure is distributed unevenly from one location to another, a similar travel time range could shape the property’s catchment commercial areas of different sizes. Also, commercial area size around the same property can vary over time due to peaks of traffic, or changing conditions of transport infrastructure, so we measure it based on up-to-date travel times.
So, the location of a property is an area around it, not a lat/long position. The parameters of this area are technically attributed to the property, but they describe the commercial area around it, not the property per se.
The higher both commercial potential and sustainability of the area around the property, the lower the liquidity risk of this property, and the wider range of potential best uses this property could have.
We differentiate “delimitation” of the property location area, and its further “description”. These are two different processes. First we “define” the area, and then we “describe” it.
We chose the amount of “active population” (people that move and spend) in the area as a proxy for its commercial potential. We chose the diversity of business functions in the area as a proxy for its commercial sustainability. We merged both proxies to derive an aggregated measure of the overall commercial potential of the area.
Then we rank all the areas in the country (globe, or in any of its parts) by the size of their commercial potential. The whole process is automated and almost instant. Location risk score (LRS) is the rank of the property location in the list of all other locations.
Location risk score is always asset type-, or function-specific. There is no such thing as a universal location score. For example, a small retailer’s location risk scores would target locations bounded by 20 min walking isochrones, as opposed to malls with 10–20 min by car or public transport.
This is what makes LRS instrumental, as it is not just a measure of location commercial power, it is a pointer on the “use”, or “function”, that may leverage this commercial power of the location best of all. In other words, LRS associated with a property, being a function- or use-specific, can provide the list of “best uses” for every location.
The amount of “best uses” for a given property is the great liquidity risk metric. The more ‘best uses’ property has the lower liquidity risk associated with it.
In our terms, the “best use” of the property is a derivative of its location. Habidatum Chronotope platform easily identifies this derivative in an automatic fashion for each and every location and compares it with the ‘current uses’ of all the locations. And what a surprise: there are multiple mismatches between “current” and “best”, which opens up information on financial “losses” and “gains’ of multiple property owners, lenders and investors. Let us keep this topic for the following article.
You can find out more about Location Risk Score here:
- Seeking a Competitive Edge, CRE Investors Embrace Non-traditional Data Sources
- The Age of Data in Real Estate
- Key takeaways from our second lecture at the MIT Center for Real Estate
- The financial cost of the wrong location
- Scoring commercial real estate for location risk reduction
- Measuring location risk for commercial portfolio management strategies
- New York offices pricing analysis using Habidatum’s Location Risk Score
- Commercial Brokerage and Leasing by Habidatum in PropTech Challenge — 2020 REBNY Demo Day
