How to Compare Crime Risk Across Multiple Locations: A Complete Framework

What It Means to Compare Crime Risk Across Locations (Direct Answer)

Comparing crime risk across multiple locations means normalizing incident data from a common spatial unit and standardized crime categories so that sites in different cities, states, or countries can be ranked on equal terms. This process serves security professionals, corporate risk managers, and site selection teams who need to allocate budget, staffing, and attention across a portfolio rather than a single address. Done correctly, it requires four things working together: a shared geographic unit of analysis, a population or activity-based denominator, a consistent time window, and crime categories that mean the same thing everywhere they are applied. This article covers both the methodology behind valid comparison and the specific tools practitioners use to execute it.

A regional credit union learned this the hard way. Two of its branches sat 1.7 miles apart, in what district-level reporting classified as the same "moderate-risk district." When the organization moved to address-level scoring, it found a 23-point BaseScore™ difference between the two sites, a gap large enough to justify entirely different security postures. Prior to that shift, a healthcare provider described a similar problem in its own words: "Our threat assessment process lacked standardization. Each region had different methods for determining high-risk areas." Both cases point to the same lesson: without a consistent methodology, crime risk comparison produces numbers that look precise but are not comparable at all.

Why Raw Crime Counts Are Not Comparable

Raw incident counts mislead because they ignore population. A city that recorded 1,000 thefts last year with 2 million residents is statistically safer than a city that recorded 400 thefts with 50,000 residents, even though the second city's number looks smaller on paper. Comparing locations on absolute counts alone rewards larger populations for having more total crime and penalizes smaller ones for having less, which is the opposite of what a security or real estate decision needs. Crime rate: the number of reported criminal incidents per 1,000 (or 100,000) residents within a defined geographic area and time period. Every valid cross-location comparison starts by converting raw counts into a rate.

The Four Requirements for a Valid Cross-Location Comparison

A defensible comparison across locations requires all four of the following:

1. A common spatial unit. Every location must be measured at the same geographic resolution, whether that is a fixed radius, a census block, or an H3 hex cell, not a mix of city-level data for one site and address-level data for another.
2. Rate normalization by population or daytime population. Incident counts must be divided by a consistent denominator so that sites of different sizes can be ranked fairly.
3. A consistent time window. All locations must be scored over the same period, typically a rolling 12 months, to avoid comparing a site's worst month to another site's best year.
4. Standardized crime category definitions. "Assault," "robbery," and "burglary" must mean the same thing across every jurisdiction included in the comparison, not whatever each local agency's criminal code happens to say.

Skip any one of these and the resulting comparison will look authoritative while quietly misranking your portfolio.

The Three Primary Methods for Comparing Crime Risk

Practitioners generally reach for one of three approaches to compare crime risk across a portfolio: calculating rates manually from open data, using a pre-built commercial index score, or using predictive risk modeling that incorporates environmental factors alongside historical incidents. Each trades off cost, speed, and completeness differently, and the right choice depends on how many locations are involved and how much is riding on the decision.

Method 1: Calculating Crime Rates Manually Using Open Data

The manual approach starts with pulling incident counts from the FBI Crime Data Explorer or a local government's open data portal, joining those counts to U.S. Census Bureau population figures for the same geography, and calculating a rate per 1,000 residents. From there, an analyst typically maps the results in a GIS tool such as ArcGIS or QGIS to visualize spatial patterns. The method is free, but it has real limits. Many agencies do not publish complete data, so gaps in the source distort the picture. There is also a structural mismatch between how the FBI's Uniform Crime Reporting (UCR) program aggregates data at the city level and the address-level precision most security and real estate decisions actually require. This gap is well known in the industry. CAP Index, one of the longest-standing commercial crime risk providers, has stated publicly that the FBI itself discourages using UCR data to rank or compare cities, precisely because the aggregation method was never designed for that purpose. A financial institution that relied on this manual approach before adopting a commercial platform described the cost directly: "Each assessment required a full week to complete," and "data collection was manual and inconsistent across regions."

Method 2: Using Pre-Built Crime Risk Scores and Indices

Crime risk score: a normalized index that quantifies the likelihood of criminal incidents occurring at or near a specific address, expressed relative to a national or regional baseline. Commercial and semi-commercial platforms generate these scores by pulling from many more sources than a single agency feed, modeling for gaps in reporting, and normalizing results so that a score in one city means the same thing as the same score in another. That combination makes pre-built scores materially more reliable than DIY calculations for multi-location portfolio work, where consistency across dozens or hundreds of sites matters more than any single site's precision. A regional credit union used a 0 to 100 BaseScore to build a quintile-based benchmark system across more than 30 branches, replacing an ad hoc, district-by-district process. A healthcare provider used the same quintile structure to establish a standardized reporting cadence and consistent security protocols across more than 1,100 zip codes, something its prior region-by-region approach could not deliver.

Method 3: Predictive and Trend-Based Crime Risk Modeling

The most sophisticated tier goes beyond aggregating what already happened. Daytime population: the number of people present in an area during working hours, which differs significantly from residential population in commercial districts and is a more accurate denominator for crime risk in business settings. Predictive and trend-based modeling incorporates environmental variables like this alongside lighting conditions, foot traffic, and the density of nearby businesses, then uses machine learning to project how risk is likely to shift, rather than only reporting where it has been. The distinction matters: retrospective scoring aggregates historical incidents into a snapshot, while predictive modeling adds environmental context and forward-looking pattern detection on top of that snapshot. This tier is typically reserved for corporate security, executive protection, and high-stakes site selection decisions, where the cost of being wrong is high enough to justify the added sophistication. A discount retailer used time-of-day and day-of-week crime pattern analysis combined with automated change detection to go beyond a static retrospective score, allowing its security team to flag which locations were trending toward higher risk before that risk showed up in a lagging incident report.

Key Variables That Distort Crime Risk Comparisons (And How to Control for Them)

Even practitioners who understand the basic methodology often get tripped up by a handful of specific data quality issues. These variables explain why two organizations can run what looks like the same comparison process and land on meaningfully different, and sometimes contradictory, conclusions.

Jurisdiction Fragmentation and Multi-Agency Coverage

Most cities are not policed by a single agency. Municipal police, campus police, transit authorities, port authorities, and county sheriffs often all have jurisdiction over overlapping or adjacent territory, and crime data pulled from only one of them is incomplete by definition. Location Inc. addresses this by aggregating data from more than 18,000 U.S. law enforcement agencies into a single location-centric database, an approach that stands in direct contrast to the FBI's UCR system, which remains agency-centric rather than location-centric. A regional credit union felt the effect of this fragmentation firsthand: relying on district-level data "forced identical security strategies across all locations regardless of actual local risk," because that data could not reveal the "granular threat variations between individual branches" hiding underneath it.

Resident vs. Daytime Population as the Denominator

A downtown commercial district might have 10,000 residents on paper but 150,000 daytime workers moving through it every business day. Normalizing crime by residential population alone in a setting like this drastically overstates the actual risk per person present, since the true population at risk is many times larger than the census figure suggests. Sophisticated platforms account for daytime population; basic ones typically do not, which is one of the fastest ways to spot a comparison that will not hold up under scrutiny.

Daytime population caveat: A global consultancy managing offices across "complex urban environments" found that daytime population differed dramatically from residential population at many of its sites, a gap that would have gone unaccounted for using residential-only normalization.

Reporting Rate Variation Across Jurisdictions

Not every crime that occurs gets reported to police, and reporting rates vary meaningfully by crime type, jurisdiction, and year. The scale of this problem became concrete in 2021, when nearly 40% of U.S. law enforcement agencies, including the New York Police Department and the Los Angeles Police Department, failed to submit crime data to the FBI at all under the transition to the National Incident-Based Reporting System (NIBRS). Any comparison built on a single-agency reporting pipeline inherits that gap. Base Operations aggregates from more than 25,000 global data sources specifically to avoid dependence on any one agency's reporting behavior in a given year.

The Ecological Fallacy: Why City-Level Data Misleads Site-Level Decisions

Ecological fallacy: the error of assuming that crime patterns observed at a city or regional level apply uniformly to individual neighborhoods or addresses within that area.

CAP Index has documented that there is commonly more variation in crime among neighborhoods within the same city than there is between most cities. A regional credit union saw this directly: "Threat environments weren't just varying between districts. They were varying dramatically between branches within the same district, sometimes within the same mile radius," a pattern that produced a 23-point BaseScore difference between two branches just 1.7 miles apart.

Crime Type Definitions Vary by Jurisdiction

Aggravated assault, robbery, and burglary are not defined identically across state criminal codes, which means a raw comparison of "assault rates" between two states can be comparing two different legal categories without anyone realizing it. NIBRS: the National Incident-Based Reporting System, the FBI's current standard for collecting crime data from law enforcement agencies, which replaced the older, more limited UCR summary system. A standardization layer that maps varied local definitions onto consistent categories is required before crime types can be compared across jurisdictions with any confidence.

Tools and Platforms for Multi-Location Crime Risk Comparison

Enterprise Platforms: Address-Level Scoring for Portfolio Analysis

Enterprise platforms are purpose-built for multi-location, portfolio-level comparison rather than one-off lookups. CAP Index's CRIMECAST Score produces 108 crime risk scores spanning 12 offense types across three geographies and three time periods, scaled against a national baseline of 100, where scores above or below 100 represent above- or below-average risk. Base Operations, Applied Geographic Solutions, Pinkerton, and Verisk's ISO Crime Service occupy the same tier, each combining broad data aggregation with a scoring methodology designed to hold up across hundreds or thousands of locations at once. A financial institution used standardized BaseScores to compare risk levels across different neighborhoods simultaneously, replacing a process that had previously required assessing each site in isolation. A Fortune 10 company consolidated more than 500 locations across 35 markets into a single unified dashboard, retiring the scattered tools its regional teams had relied on before.

At this tier, the score itself is only the starting point. A regional credit union used its scores to build a tiered response framework: locations scoring 60 or above (High/Very High) received enhanced guard presence and detection technology, locations scoring 40 to 60 (Medium) received standard protocols, and locations scoring below 40 (Low) received baseline access control only. That kind of threshold-based tiering, rank every location, set a trigger score for additional investment, and assign a response protocol to each tier, turns a list of scores into a resource allocation plan.

Base Operations delivers portfolio crime risk analysis at address-level granularity, aggregating from 25,000+ global data sources with sub-mile resolution and monthly refreshed scoring. It does not offer real-time alerts. Scores update monthly, and customers who want to trigger internal notifications on score changes can do so through the API, which fires on monthly data updates rather than live events. This makes Base Operations a complement to event-driven alert platforms rather than a replacement for them: alert platforms handle the moment an incident occurs, while Base Operations handles the persistent threat landscape underneath that moment.

Mid-Tier Commercial Platforms: National Coverage with Address-Level Granularity

Mid-tier platforms include NeighborhoodScout and Location Inc.'s CrimeRisk and SecurityGauge products, Esri's ArcGIS CrimeRisk Index, and LexisNexis Community Crime Map. These tools offer a lower entry cost and faster deployment than enterprise platforms, and they are a strong fit for real estate due diligence, insurance underwriting, and small or mid-sized business security programs. What they typically lack, compared to the enterprise tier, is deep customization and the kind of portfolio workflow integration that lets a security team manage hundreds of locations through a single dashboard with API-driven updates.

Free and Open-Source Tools: Suitable for Preliminary Analysis Only

The FBI Crime Data Explorer, the NORC Live Crime Tracker, CrimeGrade.org, CrimeMapping.com, and Numbeo all offer some form of free crime lookup. Each has real limitations for enterprise use. The FBI Crime Data Explorer reports at the agency or city level with a reporting lag of roughly two years, which makes it useful for research but not for time-sensitive site decisions. CrimeGrade.org produces consumer-facing letter grades rather than a granular, defensible score. Numbeo relies on crowdsourced perception data rather than statistically modeled risk, which measures how safe people feel rather than what the underlying data shows. None of these tools were built to support a defensible, multi-location comparison at enterprise scale, and none should be treated as sufficient for that purpose on their own.

Comparison Table: Crime Risk Platforms by Use Case

How to Find Crime Statistics for an Area

Finding crime statistics for a specific area breaks down into three tiers, depending on how much precision the decision requires. For a quick, free lookup, the FBI Crime Data Explorer and CrimeGrade.org provide city or neighborhood-level data, though both are best treated as directional rather than precise. For address-level precision on a single location, commercial platforms like NeighborhoodScout provide a modeled score tied to a specific address rather than a broader area. For programmatic access across many locations at once, enterprise options like the Base Operations API or Esri's CrimeRisk Index return standardized data that can feed directly into an internal dashboard or risk model. The key distinction to keep in mind: checking crime by zip code aggregates everything within that postal boundary, checking by neighborhood aggregates within an informal or census-defined area, and checking by exact address ties the result to the specific site in question, which is the level of precision most security and real estate decisions actually need.

What Are the Three Methods for Measuring Crime?

Crime is measured in three primary ways. The first is police-reported incident data, collected through the UCR and, increasingly, NIBRS, which captures crimes that victims or witnesses reported to law enforcement. The second is victimization surveys, most notably the National Crime Victimization Survey (NCVS), which asks a representative sample of the population directly whether they experienced a crime, capturing incidents that were never reported to police. The third is self-report studies, typically used in academic research, where individuals report on their own behavior or victimization outside a formal survey structure. Each method captures something the others miss, and each has blind spots. This is precisely why platforms that rely on UCR data alone inherit UCR's coverage gaps: they are only ever as complete as the agencies that chose to report into that one system.

UCR vs. NCVS: Which Is More Accurate for Location Comparison?

Neither is sufficient on its own for location comparison. The UCR counts crimes reported to police and is tied to a specific reporting agency, but it misses crimes victims never reported and suffers from the agency non-reporting gap described above. The NCVS captures unreported crime through a victim survey, but it produces national and regional estimates rather than address-level or even city-level data, which makes it useless for comparing one site against another. For multi-location business decisions, this means relying on either source in isolation will produce an incomplete picture. Commercial risk scoring platforms address this by synthesizing both: pulling location-tagged incident data where it exists and layering in modeled estimates that account for the reporting gaps both UCR and NCVS leave behind.

What Is the Most Common Method for Displaying Geographic Crime Patterns?

Two formats dominate: choropleth maps and heat maps. A choropleth map shades predefined areas, such as census tracts or zip codes, according to their crime rate, which makes it easy to compare defined boundaries at a glance but can visually overstate uniformity within a large area. A heat map instead uses continuous density-based shading that is not constrained by administrative boundaries, which better reflects how risk actually clusters around specific streets or blocks rather than snapping to a zip code line. Location Inc.'s SecurityGauge product explicitly rejects census-tract-boundary choropleths in favor of continuous gradient mapping at 10-meter resolution, on the reasoning that real-world risk does not respect zip code boundaries, and a map that pretends otherwise will mislead the security team using it.

A Step-by-Step Process for Comparing Crime Risk Across Your Locations

Step 1: Define Your Comparison Parameters

Before pulling any data, define three parameters that will govern every location in the comparison: the geographic unit (a fixed address, a census block, or a radius), the crime type scope (all crime, violent crime only, property crime only, or specific offense types relevant to the business), and the time window, with a rolling 12 months as the standard default. One use case demonstrated the value of this step directly: examining the geographic distribution of facilities across a portfolio, noting the range of risk levels present, and identifying clusters of facilities sitting in similar risk zones, all of which is only possible once the comparison parameters are fixed up front.

Step 2: Choose a Data Source Consistent Across All Locations

Mixing data sources across locations destroys comparability even if each individual source is reliable. If a portfolio spans 50 locations across 12 states, every score in that comparison needs to come from the same platform, using the same methodology, applied consistently. A healthcare provider standardized on a single BaseScore quintile framework across more than 1,100 zip codes for exactly this reason, replacing a prior approach that had produced "inconsistent results" because "each region had different methods for determining high-risk areas."

Step 3: Normalize for Population and Time

The core normalization formula is straightforward: (incidents divided by population) multiplied by 1,000 equals the crime rate per 1,000 residents. For commercial districts, substitute daytime population for residential population wherever the two diverge meaningfully. If using a commercial scoring platform instead of a manual calculation, confirm the platform normalizes automatically rather than reporting raw counts. A regional credit union built its quintile-based benchmark system directly on top of a 0 to 100 BaseScore that had already been normalized this way, which is what made a fair, tiered comparison across branches possible in the first place.

Step 4: Score and Rank Your Location Portfolio

Once every location has a normalized score, build a ranked risk register from highest to lowest. The goal of this step is identifying which locations sit above a defined threshold that should trigger additional security investment. A global consultancy's internal locations dashboard ranked sites by risk and tracked changes in threat levels over time, while a regional credit union used its tiered benchmarks (60 and above for High/Very High, 40 to 60 for Medium, below 40 for Low) to turn a ranked list into a concrete action plan.

Step 5: Validate Against On-Site and Incident History Data

External data should be validated, not taken at face value. CAP Index frames this as a "three legs of the stool" framework: site-specific crime risk data, incident history at the location itself, and survey data covering factors like distance to highway exits, proximity to police stations, and nearby attractive nuisances. A discount retailer put this into practice when it found that a spike in store incidents "directly correlated with increasing crime rates in the surrounding neighborhood," confirming that its external risk data and internal incident history told the same story rather than two disconnected ones.

Step 6: Re-Score on a Defined Cadence

Crime risk is not static, so a comparison run once and never updated will drift out of date. Define a re-scoring cadence based on risk tier: annual review for low-risk locations, semi-annual or quarterly for high-risk or high-value sites. The spatial patterns of crime shifted meaningfully after COVID-19, which is a concrete reminder that yesterday's risk map is not a permanent picture. A global consultancy used automated change detection to flag locations experiencing significant month-over-month crime increases, while a healthcare provider added roughly 30 new zip codes each quarter under the same consistent security protocols it used for its existing footprint.

Common Mistakes When Comparing Crime Risk Across Locations

Even experienced teams fall into a predictable set of traps when building a multi-location comparison. Watch for these:

• Comparing raw counts without normalizing for population. A larger city will almost always show a bigger raw number even when it is statistically safer per capita.
• Using city-level data for neighborhood or address-level decisions. This is the ecological fallacy in practice, and it is one of the most common ways a comparison looks rigorous while being wrong.
• Mixing data sources across locations. A healthcare provider's prior approach produced "inconsistent results" because "each region had different methods for determining high-risk areas," which made cross-region comparison meaningless.
• Ignoring daytime population in commercial area comparisons. Residential-only normalization overstates risk in business districts where far more people are present during working hours than the census reflects.
• Treating a single crime risk score as the full picture. A regional credit union's district-level data "completely masked" real differences between its branches, including a 23-point BaseScore gap between two sites just 1.7 miles apart.
• Relying on UCR data without accounting for agency non-reporting gaps. The 2021 reporting gap, when close to 40% of agencies did not submit data, is a reminder that police-reported data is never fully complete.
• Confusing perceived safety with statistically modeled risk. Crowdsourced tools like Numbeo measure how safe people feel, not what the underlying incident and modeling data actually show. A discount retailer avoided this trap by combining external risk data with internal incident history and law enforcement partnership rather than relying on any single source.

How Base Operations Supports Multi-Location Crime Risk Comparison

Every challenge raised in this article maps directly to a specific Base Operations capability. Jurisdiction fragmentation and single-agency reporting gaps are addressed by aggregating more than 25,000 global data sources rather than depending on any one agency's feed, with analysis down to a sub-mile radius so district-level averaging never masks address-level variation. Organizations managing large footprints, including a Fortune 10 company with more than 500 locations, a regional credit union with more than 30 branches, a healthcare provider with more than 1,100 zip codes, and a global consultancy with more than 75 offices, have all used the platform to standardize scoring across a portfolio rather than manage each region on its own logic.

For teams building internal risk models, API integration for crime data lets a financial institution and a regional credit union both ingest BaseScore and change-detection data directly into their own systems rather than exporting reports by hand. That integration compounds into measurable time savings: the financial institution cut site assessment time from a full week to a single day, a five-times improvement, while the credit union reduced its quarterly assessment workload by 80%, from more than 40 hours down to roughly 8. Those time savings translated into measurable outcomes elsewhere in the portfolio too. A discount retailer saw a 75% reduction in incidents after using address-level threat assessment to guide resource allocation, alongside a 66% decrease in crime in the areas surrounding its highest-risk stores, and a regional credit union documented $180,000 in annual savings from its tiered response framework. Month-over-month change detection, used by both a discount retailer and a global consultancy, gives portfolio teams an automated signal for which locations are trending toward higher risk between formal re-scoring cycles, without requiring continuous manual monitoring.

Ready to see how a standardized score would rank your own footprint? Get a custom BaseScore report for your locations and see where the biggest gaps in your current comparison process are hiding.

Frequently Asked Questions About Comparing Crime Risk

How do I check crime rates by zip code or address?

For a quick, free check, use the FBI Crime Data Explorer or a local police department's open data portal, both of which typically report at the zip code or agency level. For address-level precision, commercial platforms like NeighborhoodScout model risk down to a specific address rather than an entire zip code. For programmatic, multi-location access across an entire portfolio, enterprise platforms such as Base Operations or CAP Index provide API-based scoring. Zip code data aggregates a wider area; address-level data ties the score to the exact site in question, which matters most for security decisions.

What is a crime risk score and how is it calculated?

A crime risk score is a normalized index that quantifies the likelihood of criminal incidents occurring at or near a specific address, expressed relative to a national or regional baseline. It is calculated by fusing data from multiple sources, modeling to fill gaps in agency reporting, and scaling the result against a reference point so scores are comparable across jurisdictions. CAP Index uses a 100-point national baseline; Base Operations uses a 0 to 100 BaseScore. Both approaches convert raw, incomplete incident data into a single comparable number.

Can I compare crime risk between cities using free data?

Partially. Free sources like the FBI Crime Data Explorer can support general city-level trend comparisons, but they are unreliable for address-level or multi-jurisdiction portfolio decisions. The FBI itself discourages using UCR data to rank cities, agency reporting gaps mean the underlying data is incomplete, and free tools do not normalize for population or daytime population the way commercial platforms do. Free data is a reasonable starting point for directional research, not a substitute for a normalized, defensible comparison.

What is the difference between a crime rate and a crime risk score?

A crime rate is a simple calculation: reported incidents divided by population, typically expressed per 1,000 or 100,000 residents. A crime risk score is a more sophisticated index that incorporates modeled data to fill reporting gaps, normalizes across jurisdictions, and in some cases adds forward-looking projections. A crime rate tells you what was reported; a crime risk score adds the comparability and gap-filling that make cross-location ranking possible.

How often should organizations re-evaluate crime risk for their locations?

Annual review is appropriate for low-risk locations, while semi-annual or quarterly review is recommended for high-risk or high-value sites where conditions can shift faster. Organizations with continuous portfolio monitoring needs often supplement scheduled reviews with automated change detection, which flags locations experiencing significant month-over-month shifts in crime patterns between formal re-scoring cycles.

What data sources do commercial crime risk platforms use?

Commercial platforms typically combine police reports (UCR and NIBRS data), direct feeds from individual law enforcement agencies, court records, media monitoring, U.S. Census Bureau population and daytime population estimates, and in some cases proprietary environmental data covering factors like lighting and foot traffic. This multi-source approach is what differentiates commercial platforms from free tools that rely on a single federal data feed, which inherits that feed's coverage gaps and reporting lag.

Is UCR or NCVS data better for comparing locations?

Neither is sufficient alone. UCR data is tied to a specific location or agency but is incomplete: nearly 40% of agencies, including the NYPD and LAPD, did not submit data to the FBI in 2021. The NCVS captures crimes that were never reported to police, but it produces only national and regional estimates, not location-specific figures. Commercial risk scoring platforms address this gap by synthesizing both sources rather than relying on either in isolation.

What does "normalized crime data" mean?

Normalized crime data has been adjusted for population, time period, and geography so that locations with different sizes and different agency reporting standards can be compared on equal terms. Without normalization, a large city will always appear to have more crime than a small town in raw numbers, even when the small town has a higher rate of crime per resident. Normalization is what makes a fair, apples-to-apples comparison possible.

How accurate are commercial crime risk scores compared to actual crime outcomes?

Independent, publicly available benchmarking of commercial crime risk scores against actual crime outcomes is limited across the industry; this is a known gap rather than something specific to any single vendor. Some providers, including Verisk, reference actuarial validation against insurance loss outcomes. Buyers evaluating a platform should ask directly about its validation methodology, its update frequency, how transparent it is about coverage gaps, and how it fills those gaps when agency data is missing.

What is the ecological fallacy and why does it matter for crime risk comparison?

The ecological fallacy is the error of assuming that crime patterns observed at a city or regional level apply uniformly to individual neighborhoods or addresses within that area. It matters because, as CAP Index has documented, there is commonly more variation in crime among neighborhoods within the same city than there is between most cities. A regional credit union saw a 23-point BaseScore difference between two branches just 1.7 miles apart, both of which had been classified under the same "moderate-risk district" label, a gap that district-level data alone would never have revealed.

Ready to move past city-level averages and see exactly where risk varies across your own footprint? Request a demo and get a portfolio-wide, address-level comparison built on standardized BaseScores.