What’s happening, when is it happening and where is it happening? All good market research starts with the answers to some key questions. When it comes to the “where,” location intelligence is the vital data set that drives decision-making.
The real estate sector is advancing at a lightning pace. This growth is accelerated by new resources, including location intelligence platforms like the one offered by Unacast.
Innovators like Unacast are finding new ways to make sense out of the ocean of data that answers the “where.” A high-level of understanding is activated when you can literally take a 30,000 foot view and see traffic, patterns, and point of interest (POI) data. This is unlocking new opportunities for real estate investors, developers and planners around the world.
To learn more about real estate location intelligence, read on.
Real estate investment and development decisions are strategic. With multi million or billion dollar risks and rewards, leaders in the real estate industry have to be armed with relevant, timely info. Having the right location intelligence is mission critical to driving firms forward and avoiding debilitating financial losses.
How do real estate investors, developers and other professionals make the right decisions? Through real estate location intelligence. Unfortunately, real estate data is often limited, and real estate data analytics is treated more like a guessing game than science.
There is a better way, and Unacast has built it. Blending point of interest data, location data streams and groundbreaking analytics tools, Unacast’s Data Portal provides actionable insights that allow real estate professionals to build profitable and diverse portfolios.
Of course, the benefits are only achievable with the right foundation.
Location intelligence refers to the ability to gather, blend and visualize large amounts of data from multiple sources. This should occur both at a broad, national level and on a granular, neighborhood level.
Location intelligence platforms and real estate data providers gather and synthesize data on points of interest, migration patterns, foot traffic, industry trends, demographic and population information and more.
Location intelligence isn’t truly intelligent unless it is displayed in a way that allows for quick and accurate interpretation. This has been the missing piece in the point of interest data hunting and gathering of the last couple of generations. Often, insights are gleaned but, by the time they are translated, it is too late. Trends change fast.
Location data is more a raw resource than a tool. Data visualization is the tool that bridges the gap between raw data and intelligent action. Of course, even the visualization method is important.
Points of interest, places data, location intelligence: all of these data sets need a shared format. This calls for highly functional and customizable data visualization tools. In addition to intuitive charts and graphs, Unacast provides location intelligence data visualizations in the following formats, among others:
Point distribution maps.
These maps display each data point as a literal point in its location. The points can symbolize any item or area of interest and provide a quick look at the location density of the particular data set you are analyzing.
This data visualization technique is particularly useful for population visualization in real estate data analytics. A two-color system displays the density of population or another data point in a given location, with a darker or more concentrated color signifying a higher density.
Similar to heat maps, choropleth maps use color darkness to represent a higher proportion or percentage of any given data point in a predefined area. This technique is often used at the national level to visualize data for states or provinces.
In real estate data visualization, flow maps are often used to understand migration patterns to and from states, cities and neighborhoods. Arrows or lines represent movements, and line thickness often represents a higher proportion of people moving in a particular direction.
Proportional symbol maps.
This data visualization technique uses a symbol, such as a circle, to represent data points. The larger the symbol, the higher the concentration of data points in a given location.
The user can configure each of these formats to match their location preferences, and each visualization type lends itself to particular types of location data. But each map-based visualization format begins with point of interest data.
This complex topic can be summed up rather simply: point of interest data is data about points of interest. Yes, the sentence is just flipped. But since it isn’t good linguistic practice to define a term by itself, let’s dive deeper.
A point of interest is a physical location that may be of interest to a consumer. Grocery stores, parks, restaurants, bars, office buildings and similar places can be points of interest. POI data goes beyond simply identifying these places. POI data maps points of interest by longitude and latitude and assigns them categories like those included in the North American Industry Classification System (NAICS).
It is helpful to think of POI in context:
An easy way to picture POI data in a real-world setting is to get out your smartphone and open Google Maps. In the area surrounding your current location, you will see several prominent locations that may be of interest to you, including restaurants, cultural attractions and parks.
The generic list of all POI data points is visible until you enter a search query that narrows the POIs, such as “restaurant.” Now, you have adjusted the data set — visible POIs should only include those that are relevant to your query.
The above example involving Google Maps may beg the following question: why worry about obtaining POI real estate data when Google Maps is free and available to anyone? It’s a good question.
Here’s why: Google Maps and similar platforms rely heavily on business owners and those in charge of other points of interest to feed information into the platform.
Human error alone introduces enough uncertainty to make this type of readily available POI data unreliable. For real estate investors and developers with millions on the line, this kind of location information is insufficient.
In addition, even without errors at the time of information input, POI data on these platforms quickly becomes outdated when business owners fail to update their information in the system. This is immediately obvious if you’ve ever used Google Maps to get somewhere, only to find that the business has moved.
So, if these open-sourced location data platforms aren’t good enough, what is?
Real estate data providers provide accurate and reliable POI data. They do this in several ways, and the exact method varies from provider to provider. The best POI location data usually comes from a combination of approaches that takes the best of each method.
Here are the traditional methods of obtaining POI data:
Actually going to a location, walking around and recording POIs is the old-school method of POI data generation. Compared to automated and digital POI data extraction methods, this method is slow and inefficient. Of course, what it lacks in speed, it makes up for in accuracy. A trained data professional, manually obtaining POI data, is much more likely to provide accurate information than a layman feeding information into a GPS app.
While this POI data generation method is not possible in some countries, in countries that do make business registration information public, this method can be highly effective. In most places around the world, businesses must supply key information—including their business type and address—to obtain a license. Where records of business registrations are publicly available, this can be a trustworthy source of reliable POI data.
Location engines, like Google Maps and similar platforms, have a wealth of POI data that many data providers scrape with sophisticated software to generate huge data sets. This is the quick, relatively easy and sometimes dirty way to get POI data.
Many of these platforms actively discourage data scraping, so in some cases, the ethics of taking POI data from these platforms can be questionable. Also, as mentioned above, data from these open platforms rely on human input and can be inaccurate or outdated.
More than 85% of Americans own a smartphone, and smartphone ownership is increasing all around the world. Many of the apps available for download on smartphones encourage users to generate POI data points, often inadvertently.
Checking in to a store or restaurant on social media, allowing an app to track your real-time location and even uploading a photo that contains location metadata can all generate POI data points. User-generated POI data is nearly endless, but it is not always accurate. Location settings and hardware problems can sometimes muddle these data sets.
POI data is only one layer of real estate data—albeit a critically important one—necessary to conduct intelligent real estate data analysis. A simple POI data set provides the foundation on which you can apply other data sets, including data about foot traffic, mobility, neighborhood density and more.
One key example of how location intelligence data in real estate is diverse and constantly growing is the internet of things (IoT). This vast network of internet-connected and location-enabled devices is growing at a rapid pace. It provides a previously unachievable depth of data that is applicable to location concerns.
As IoT devices become more common, they generate more data on user location, foot traffic, consumer behavior and more.
A blend of data streams fuels the real estate insights that drive development and investment decisions. This is real estate location intelligence: technology, geographic information systems (GIS) tools and data come together to allow real estate professionals to visualize and use spatial data to make informed decisions.
The applications of real estate location data analytics are virtually limitless. Site selection, demographics, neighborhood trends, emerging areas and competitor insights no longer have to rely on guesswork and gut feelings. Firms that continue to ignore real estate location intelligence are well on their way to falling behind their competitors.
Here are some of the key applications for location data in real estate:
Some commercial real estate (CRE) professionals may remember the days of having to count cars in parking lots at various times of the day and the long hours watching consumers filter in and out of a community. These were the early steps of the site selection process. Now, as real estate location intelligence has evolved, those days might as well be the Stone Age.
Location intelligence provides site selection insights that drill far deeper than traffic volume. Beyond the number of consumers in a location, data scientists can see the preferences and behaviors assigned to those individuals. They can also see where those consumers went before and after they entered the area.
This kind of revealing consumer data couples well with real estate data about similar areas. With access to the right location data sets, you can see the factors that have made comparable sites successful. When the data reveals a win, investors can look for other locations with the same features. Insights like this have the potential to identify marketplace gaps and investment opportunities.
A critical aspect of real estate location intelligence is the ability to understand the consumers. Household income, median age, education level and marital status are just the beginning of important demographic data that should precede real estate buys.
With location intelligence, you can see dozens of demographic trends in a neighborhood, then compare those to the neighborhood that already has a proven record of return for investment. It’s also a way to see behind the curtain of competitor sites or holdings.
Understanding traffic flow, commutes, residential occupancy and commercial growth is vital for real estate investors. With access to the right combination of real estate data sets, a clear picture begins to enter the frame.
Using real estate location intelligence, you can see the following:
Daily, weekly, monthly or seasonal traffic flow patterns into and out of the neighborhood
The distance people are traveling to reach the neighborhood
Housing occupancy and residential trends, such as migration
The average addressable market for any commercial or residential venture
POI and location data don’t just tell you about current state: they are useful in forecasting as well.
Capitalizing on emerging areas or hot markets is one of the most effective ways to generate explosive ROI in real estate. But identifying these areas before competitors is the only way to pull it off. This has proved problematic for real estate firms that rely on widely available location data and demographic information that is slow to reflect real-world change. Census and IRS data alone simply won’t cut it. These data sources are cyclical and do not provide information fast enough to inform investment decisions.
Take, for example, the COVID-19 pandemic. As the pandemic broke out in early 2020, residents of large cities suddenly began moving to more rural areas. This migration was fueled by a fear of densely populated areas and the rise of remote work. Anecdotally, everyone knew this was happening, but you would find no indication of this migration if you reviewed census data.
The newest census data reveal what everyone already knew. But if a firm had been relying on census data available in March of 2020 to inform real estate investment decisions, they might have bet big on big cities at exactly the wrong moment.
Real-time real estate location intelligence data showed exactly what was happening, when and where.
Real estate data analytics and location intelligence drive firms to new heights. Used correctly, they can also drive firms to excel in the competitive landscape. Location data in real estate settings can provide information on nearly any neighborhood or property. This makes it useful to understand not only how your properties are performing, but also how your competitors stack up.
Property owners can see how often customers or clients go to competitors, and know exactly when they do it. It is also possible to compare the demographics of visitors to a property with those of the visitors to competitors. The ability to see real-time data on cross-visited brands, top competitor locations and week-over-week foot traffic at any given location provides an enormous leg up on competitors who lack the same information.
With Unacast’s data visualization tools, residential real estate investors and developers can see short and long-term migration trends, changes in population distribution and emerging areas for investment opportunities.
Location intelligence in residential real estate begins with understanding and visualizing the data from our Migration Patterns tool. Below, we take a look at some of the location data sets Unacast uses to provide the in-depth visualizations available in Migration Patterns.
Also called OD Flux, home-based origins-destination flux tracks changes in home location on an individual basis. The initial home location is the origin, and the new home location is the destination.
OD Flux begins with an assessment of the probable home location of every GPS-enabled device in Unacast’s device panel. On a weekly basis, the tool assesses the probable home location of each device, looking for any changes since the last measurement. To establish OD Flux, any changes in a device’s apparent home location need to stick for several weeks.
As changes in home location begin to populate the data set, the tool assigns migration flow to pairs of origins and destinations to reveal consistent patterns in home location changes. This allows users to see which neighborhoods’ residential real estate prospects are heating up and cooling down.
Population distribution trends (PDT) is a similar metric to OD Flux, but it does not show which devices moved to where or establish that the move is permanent. Instead, Unacast establishes PDTs by measuring the number of devices active in each county or state every week.
Unlike OD Flux, PDT provides an instantaneous measurement of population changes. This makes it useful to residential real estate firms that would like to analyze the immediate situation in a county or state.
The emerging areas measurement is an aggregate of the various data sets available in Migration Patterns. The metric examines the change in income, net flow, inflow and outflow of any U.S. census tract. Migration Patterns aggregates its weekly data once each month to reveal emerging areas.
This tool enables the following:
Real estate investment decision-makers can buy at predicted, lower prices and maximize ROI.
Leasing managers can accurately anticipate where demand for housing and services will increase beyond the current supply.
Real estate development companies can decide which plots in their portfolio should be developed and where they should obtain new properties.
CRE professionals and firms must engage in data-driven decision making to make sound investments and sustain growth over the long term. Many of the same Unacast tools used for residential real estate decision making benefit those in the CRE industry.
Mobility and point of interest data can help CRE investors make the right call, even in the most difficult investment environments, such as the post COVID-19 market.
Here is how a CRE firm could use a combination of Unacast tools to drive data-backed CRE decisions:
Analyze migration patterns.
In the Migration Patterns dashboard, investors can quickly see which states are experiencing net inflows and outflows. For example, in the wake of the COVID-19 pandemic, the densely populated states of the northeastern U.S. saw net outflows over several months, indicating that residents were fleeing densely populated metropolitan areas for suburban or rural areas.
Find growing neighborhoods and cities.
After identifying a state that is seeing a net migration inflow, you can drill down to the city level to see patterns like shifts from urban to suburban areas. Taking it one step further, you can then see migration trends and OD Flux for individual neighborhoods within desirable towns, all the way down to the smallest available unit of measurement: the Census Block Group level. The Census Block Group level includes between 300 and 6,000 people per block.
Identify retail impacts in any location.
Using the Retail Impact Scoreboard, CRE investors can gain an understanding of which industries are growing and/or recovering from impacts like COVID-19 in a given location. Available insights include most recovered industries, most rapidly recovering industries, average traffic per venue and overall recovery speed across multiple or individual industries.
Unacast commercial real estate data analytics goes beyond retail investment opportunities. Tailored location data metrics allow CRE firms to innovate and gain an edge in logistics real estate.
Evaluating tracts of land for potential distribution facilities, warehouses and fulfillment centers is difficult, if not impossible, without location intelligence information. Unacast blends various data sets to provide a portal that CREs can configure to match their needs and interests. Quickly select and modify data streams for groups of census tracts to get immediately actionable information.
With logistics real estate location data tools, you can identify and rank areas that may be viable for industrial development. This enables you to make investment decisions well before your competitors.
Commercial real estate data analytics, residential real estate data insights, migration patterns, origin-destination flux, foot traffic and POI location data come together to make Unacast the most powerful real estate data visualization tool available. Intelligent real estate investment decisions begin with real estate location intelligence.
Unacast is designed to help real estate firms grow and thrive. Ready to give it a try?
Schedule a meeting with the Unacast team: Jon Torre, Jimmy Greco, Paige Hollier, and John Ryan. Not ready to meet with us? Send us an email instead.