The financial cost of the wrong location

Best vs. current use mismatches highlighted by Location Risk Score

Anna Amvrosova
Published in
11 min readMar 2, 2021



Summary: Location has always been a key value determinant for investing in commercial real estate (CRE). However, to factor it in has always been a costly and time-consuming process with high levels of subjectivity. No matter how high the cost, location risk analysis is always performed, as the expected financial loss of the wrong location could be way higher.

Local market dynamics can change in extreme levels nowadays, conflicting with the idea that real estate is a fundamental safe haven. Creditors and investors should be concerned about potential losses that might be generated by the inability of an existing property to adjust to the dynamic local conditions. This risk may diminish the property’s derived value, and an owner may want to reprofile the property or substitute for a property in some other location where the best use is not at war with the current actual use or zoning.

Location Risk Score (LRS) is appropriate during all stages of a CRE lifecycle; from pre-purchase analysis, intra-cycle valuation calculations, to liquidation events. The CRE investors, and industry analysts, would highly appreciate an ability to quickly compare assets, put them into context with peers, and finally select the best candidates or filter the worst ones out while working on portfolio composition and maintaining an appropriate risk profile for their portfolios.

Addressing this need, Habidatum built an automated and data-driven Location Risk Scoring system (LRS) that measures the location risk for any type of property. The output of this scoring system is multidimensional: It quantifies the risk of commercial real estate location and monitors its evolution over time.

LRS defines best use of the local market, and therefore reflects potential losses related to being in the wrong location. The definition of the “wrong” represents the core of LRS methodology, that looks into the difference between current use of the property and its best use. We determine a location’s best use by collecting granular data feeds of market conditions and analyzing people’s activity and business diversity with specific adjustable accessibility parameters. The bigger the difference in score from best use to current use, the higher the risk of losing revenue generated by the property, and the less appropriate the location is for this type of CRE asset.

Explanation: Property itself, a building, is just a shape for the business function it is hosting. It is the local market around the property that gives life to its function: if a local market does not generate the demand for it, then the business may not thrive. In other words, brick and mortar define the “ability” of the owner to derive value from the property. The necessary “conditions” to derive that value are in large part defined by the property’s local market, which is by no means under the control of the owner. Property owners can manage their ability to derive value, but must seriously consider the local market conditions. According to Moody’s Analytics, local markets are responsible, on average, for about 80% of the property value.

In order to make LRS functional and instrumental, Habidatum “decouples” the location (local market conditions) from the property, measuring commercial potential/risk associated with the local market separately from the property. After the location risk score is calculated we compare the best use of the property’s location, as derived by the LRS methodology, with its current actual use.

How is the “best” determined? How does LRS reflect the “best use” of the location? Utilizing Cuebiq Workbench, we pair Cuebiq’s first-party, opted-in mobility data with business diversity data. We then put the paired data into the context of “accessibility” via three modes of transportation, auto, public transit, and pedestrian, by applying travel time data to determine the local market spatial shape (the data on these catchment areas’ boundaries is provided by Travel Time).

Our first task is to determine the boundaries of the local market around the property and then fill it out with mobility and business diversity data in order to get a proxy of the local market's commercial potential. Then we cross these data feeds to derive LRS for the area defined by the travel time boundaries.

The boundaries determination processes can be split into several steps:

First, we score for the location by every time interval (5 min, 10 min, 15 min, and further on), then the best accessibility score for each mode of transportation is highlighted. Its combination reflects the best possible accessibility area the owner of the property can currently hope for. Then we select an optimal accessibility mix for the existing property, say a supermarket, assuming proper travel time for each category of clients (say, 20 min auto, 15 min public transit, and 10 min pedestrian) in the very same location and see how the average accessibility mix across all three transportation modes differs from the optimal mix calculated above.

By optimal accessibility mix for the owner, based on the business experience, considers a proper catchment area. A concrete type of commercial real estate asset in LRS methodology is only defined by an accessibility mix relevant in the owner’s terms to the catchment area of an asset. A warehouse owner may only rely on automobile accessibility, but for someone who runs a hotel, its accessibility will likely depend upon all three modes of transportation each within travel time limits set for them by the owner or industry analyst.

The difference between the best and optimal (accessibility delta) is the measure of risk for the owner of being in a wrong place, meaning a mismatch between the best use, derived from the best accessibility mix, and the one we consider an optimal mix

LRS methodology allows us to scan all the locations in the city/country/world for the best use they currently support and compare the results with the actual use of those locations by their owners. If the methodology generated “best use” for a location appears to be a warehouse, but actual use is a cinema theater, then the owner gets confronted with a challenge. As the current use of the property does not match the derived “best use” of its location then significant losses can be expected. An investor may want to adopt the property to a different function, or, possibly, get rid of the property and replace it with another one in a better location; an owner/user might consider moving their business to a location that matches their desired use.

This type of LRS measures can be monitored over time, so the owner of a property and a potential creditor/investor into the property can turn them into indices (temporal delta) making arguments for buying, selling or refinancing strategies. Using LRS as a forward-looking indicator will help owners identify opportunities and more quickly respond to changes in location dynamics.

The property’s “current use” may not be the “best use” required by the local market, but if market trends favor the “current use” of the property as the “best use” for the future, then the discrepancy between “best” and “current” can be an opportunity. A developer may be interested in fixing the function of the property and wait until it becomes the “best use”, as is typically used when valuing land. A passive investor may be interested in selling or buying a property before the market correction, as the value of the property will ultimately be defined by its “best use”. An owner/user may be interested in relocating to an area better suited to the business.

Therefore, the application value of LRS is about understanding the “future market value” of the underlying location. The “best-to-current use mismatch” may sometimes be an argument to buy the property, as the buying price defined by the “current use” may be lower than the future reselling price defined by the “best use” of the property.

What is the best use of my place? How does my optimal use fit into this place? Those questions can be easily answered by the LRS system for millions of locations across the globe.

Key words: asset/property/brick and mortar, location/localitty/local market, best use/current use/function, location risk scoring, accessibility/travel, time/isochrones/transit modes/accessibility, mixes/catchment areas, opportunity cost

Case study

The goal of this case study is to illustrate the dollar amount cost of being in the wrong location. It is seen through two metrics: the “losses of the current use”, and the “lost opportunities” compared to the best use. By comparing the local rental rates for each use we can see the financial impact of location on the property owner’s results. Properties that match the best use for their areas rent at rates above comparables and properties that do not match the best use rent at rates below comparables (the latter bear the “losses of the current use”: for example, warehouses in the locations where logistics is not the best use may rent lower than the warehouses in the appropriate best use locations). Additionally, the opportunity cost of missed rent for mismatched uses can be calculated by comparing rents to the average rate of the best use, typically much higher (the “lost opportunity”: for example, warehouses in the locations where retail (not logistics) is the best use might be renting lower than the retail properties hence are losing the opportunity).

The case study area is Miami MSA, consisting of Miami-Dade, Broward, and Palm Beach counties. A sample of properties in retail and logistics space was taken from open data sources: 58 warehouses, 133 malls as of February 2021.

LRS was calculated for the locations of the Miami MSA, with a focus on retail best use and logistics best use. As mentioned above LRS is linked to various best uses/property functions via the tuning of the size of the local market and transit modes within it taken into consideration (so-called “accessibility mixes”): for logistics, the size of the local markets is larger but not dependant on walking infrastructure; while for retail, walking accessibility and the quality of the property’s closest proximity (locality) is highly important*.

* Accessibility mixes applied for LRS calculation in this case study: for logistics/warehouses — Walking accessibility 30 min, Auto accessibility 30 min; for retail/malls — Walking accessibility 10 min + Local Score + Auto accessibility 30 min.

Then, we focused on the warehouses sample (58 properties) and their locations. Based on LRS the majority of them (63%) are more appropriate for retail function than for logistics**.

** There could be other reasons for warehouses to be in these locations, unrelated to their commercial potential and more industry-specific (not taken into consideration in the current case study).

There’s a high commercial potential in the walking distance that is disregarded by the warehouse function but can be a driving force for a successful retail business.

Then, for the selected warehouses (where retail is better use) we ran the calculations:

  • current loss or loss of the current use = selected property’s rent rate per sq ft VS. average rent rate per sq ft in its surrounding area (10 min drive) for its current use (in the current case study, warehouse where warehouse function is not the best but current use vs. warehouse where warehouse function is the best and current use);
  • lost opportunity / loss of the best use = selected property’s rent rate per sq ft VS. average rent rate per sq ft in its surrounding area (10 min drive) for its best use (in the current case study, warehouse where warehouse function is not the best but current use VS. retail property where retail is the best and current use).

The average loss is $3.7 per square foot per year. 48.6 % of warehouses have the negative delta / current loss i.e. their own rent rate per sq ft is lower than the average one among the surrounding warehouses. For example, based on this, for a 50,000 sq ft warehouse, the total annual loss of the owner would be $185k, or $15.4k monthly.

The lost opportunity compared to the retail best use happens in the case of 100% of the highlighted warehouses: all of them have rental rates lower than the malls, and the owners are losing from $4 to $100+ per sq ft per year. For example, a 50,000 sq ft warehouse could generate up to $5M more annually in case it gets repurposed into a retail venue, as retail is the best use for its location.


As it has been shown above, LRS can be very instrumental in determining potential current and losses due to the location of the real estate asset.

The primary users of this LRS system are financial institutions, managers of large portfolios of collateralized loans, commercial real estate asset managers maintaining desired risk profiles for their pools of assets, real estate analytics platforms supplying professionals with CRE information, re-insurers buying financial guarantee risk, or large commercial networks or chains optimizing their facilities across the globe.

Being especially useful for the management of massive portfolios, LRS is a quick and automated solution allowing massive comparisons of various assets. LRS shows to the owner of an asset its relative position vis-a-vis thousands of locations around the property. LRS does not only offer the owner of an asset a score of his/her location, it gives him/her much more than that — a risk comparison system calibrated with the risk information of many other locations. As a famous British historian, Arnold Toynbee, once said, “It would hardly be possible to understand English history without reference to other parts of the world” (The Study of History, Barn & Nobles book, New York)

In the nutshell the LRS system offers a brand-new solution for the CRE investors, that can:

— generate savings through better risk understanding and management: impacting rent and cap rate(#bp),

— reduce costs of underwriting candidates for the asset portfolios; Allows effective and efficient filtering out of the CRE candidates for large credit or asset portfolios through wider coverage and application of the standard location risk benchmark;

— make asset portfolios quickly scalable due to the standardization of the location comparison process;

— provide operational benchmark data-feed ready to plug into corporate financial and risk models;

— dramatically reduce the time for decision making through an automated process and standardized data processing algorithms;

— cover all types of properties (CRE to housing) and their locations;

— draw upon the global scale, global grid, and global distribution network.

Written by Cristopher Cook, William McCusker, Alexei Novikov, Katya Letunovsky, Nikita Pestrov, and Daniel Gorokhov.



Anna Amvrosova

Urban researcher and data analyst, connecting physical spaces with digital world. Passionate about cities, in love with the ocean