Enodo Multifamily Blog
Real Estate Trends, Tools, and Best Practices.

Where Does Enodo Get Its Data?

There is no shortage of data when it comes to commercial real estate, from open sources to paid data providers, there is a wealth of information in the marketplace. The difficulty is how to bring all this data together and try to make sense out of it to help make better business decisions.

Enodo automates data collection and analysis to deliver meaningful insights to users, significantly accelerating our users' collective ability to understand and act upon market opportunities. This blog post outlines the various sources and collection methods used to help Enodo’s algorithms generate the most accurate predictions in the market.

Rent and Amenity Data

Rental rates and amenity data are accumulated in three layers of granularity, which provide both the breadth and the depth of data coverage to generate accurate predictions in every market throughout the country. These layers include:

Property Management Software Integrations & CSV Uploads

Through streamlined property management software integrations, users can directly integrate their portfolio data. Individual user information is NEVER made publicly available to other users, however, our algorithms train on this meta data in real-time, facilitating more accurate predictions over time.

CSV Excel uploads enable users to manually upload property detail. Again, this data is kept confidential; however, it is used to train price prediction algorithms and deliver better results.

Individual Property Websites

Direct connections to property websites provide the second layer of granularity, allowing Enodo to monitor daily updates on newly available units and their pricing, floor numbers, views, and amenities. These sources are used to calculate individual unit availability and time on market, providing insight into occupancy and market absorption rates.

Listing Site Integrations

Integrations with listing sites provide the best breadth of coverage – particularly when it comes to listings from owners and managers who do not use property management software. Enodo currently integrates with eight different listing sites to pull updated data on a daily and weekly basis.

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Location Data

Enodo's algorithms utilize detailed demographic, economic, and locational amenity data to ensure accurate predictions. Most methods of analysis underemphasize the importance of this data, but it should not be ignored. One of the primary underpinnings of Enodo's high level of accuracy (under $60 median error on rent predictions nationwide as of this writing) is its ability to cluster market demand-side data effectively, utilizing the demographic, economic, and locational demand drivers that correlate most significatively with real estate investment potential.

ESRI Demographic Data

Enodo's primary demographic data provider is ESRI (Environmental Systems Research Institute), an international supplier of geographic information systems (GIS) software and demographic data. As price is ultimately a function of supply and demand, this detailed demographic data comprises a large component of the demand side information feeding the clustering algorithms.

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Google Places and Open Street Maps (OSM) APIs

By integrating with both Google Places and the Open Street Maps API, Enodo's locational amenity data is bolstered to ensure an accurate account of variables such as the volume and types of shopping/retail amenities, proximity to major demand drivers like universities and hospitals, and local transit availability. 

Open Data Portals and Government Sources

Proximity to rivers, lakes and beaches, or the presence or absence of major regional transportation centers (e.g. airports) all come from publicly available open data sources. As public data sources are increasing in national coverage, the automated collection of this data consistently updates the database as it becomes available. 

Public Property Data

The core physical property data is collected from both paid and public open data sources to ensure complete property records, which include year built, number of units, and total square footage. 

CoreLogic, First American Data Tree

Historical transaction data, physical property data, and property tax data are provided by CoreLogic and First American Data Tree. Enodo trains on this data to improve both pricing and comparable property detection algorithms. Historical transaction data reports are delivered to the front-end from First American Data Tree's API.

Open Data Portals

Enodo automates the collection of data from open data portals, such as the City of Chicago. Detailed information such as year built, number of units, number of stories, lot square footage, etc. are collected both to train predictive algorithms and bolster property records.

Financial Data

Property performance is ultimately summed up by its financial returns. Enodo generates income and expense predictions to determine NOI and market value by aggregating historical operating metrics, income and expense forecasts, and data on user investment criteria and available financing terms.

Operating Expense Line Items

Enodo collects operations data from thousands of properties in every major market throughout the country, and our algorithms reference historical expense differentials across markets and property types to both control for, and to accurately predict, variances in expense line items between markets.

Growth Forecasts

Enodo utilizes historical rent and expense data to analyze trends and produce forward looking projections (down to the neighborhood level) across the country. Using the hold period entered by users, Enodo blends the projections derived from short term rent and expense growth data with long term CPI growth to derive constant growth rates for income and expenses throughout the course of a hypothetical investment period.

In conclusion, Enodo's automated data collection methods provide rich content free from error-prone human entry, and scale effectively to ensure all new and meaningful data is captured. By employing proprietary data aggregation, parsing and validation algorithms, including support vector machine algorithm to smooth the seasonal price swings from various revenue optimization software platforms, Enodo can ensure a database free of outliers and anomalies.

We're happy to show you how to put this data to use on a quick demo. Use the link below to schedule a demo at your convenience. 

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Marc Rutzen
Marc Rutzen
At the intersection of Real Estate and Technology, Marc Rutzen is the CEO & CoFounder of Enodo Inc.

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