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A Deep-Dive into Commercial Property Analytics For Landlords

The commercial real estate (CRE) industry has experienced massive growth and success in terms of technological advancements to the office, bringing out new-age obstacles that landlords, property teams, and tenants have to face. As CRE adapts to a modernworkforcethrough the addition of tech-enabled consumer experiences, it is simultaneously establishing a new set of office standards moving forward. Likewise as these experiences continue to evolve the role of commercial property analytics for landlords and property teams has become a centerpiece of discussion for CRE leaders who are looking to triumph over the inevitable ebbs and flows of a recently disrupted market.

By accessing more granular real estate analytics on your buildings end-users its tenants you will be able to keep your competitive edge in the market by providing them with what theyrereallylooking for in the office.

What is Data Science In Real Estate?

According to a Towards Data Sciencearticle, there are plenty of opportunities to apply data science practices or using scientific methods, processes, algorithms, and systems to extract and apply insights and knowledge from all forms of data to commercial real estate data. As they explain:

There is huge pressure on the real estate industry to unlock the potential of big data and incorporate machine learning and evidence-based approaches in their workflows. In the KPMG Global PropTech Survey 2018, 49% of participants thought that artificial intelligence, big data, and data analysis were the technologies likely to have the biggest impact on the real estate industry in the long-term.

Consequently, some forward-thinking top executives at real estate institutions with long operating histories are pushing their firms to unlock the potential of their decades of records of transaction, valuation, asset management, listing, and other data. Simultaneously, the data provision space is maturing (possibly even overcrowding) with successful startups such as HouseCanary and Reonomy joining established players like CoStar and Real Capital Analytics, making it possible for any company interested in real estate to quickly obtain large amounts of relevant data.

However, as noted in a recent NAIOP article, real estate professionals are facing challenges figuring out how to actually utilize data. The KPMG Global PropTech Survey 2019 confirms that 80% of firms still do not have most or all of their decision making led by data. The same report also hints at a skills gap only 5% of real estate firms have transformation efforts led by someone with knowledge of data analytics.

With this in mind, there are several ways big data in commercial real estate can run through a data science model. Below are just a few:

  • Property Prices:Today, data-driven computer models account for up to80%of trading and finance transactions. Thus, data science methods can help property teams aggregate and understand large data sets to cut through the noise and understand individual sub-market performance. Throughproperty price indices, millions of rows of noisy transaction data can be combined with information about locality, property characteristics, demographics, and more, to produce granular sub-market indices. For example, indices can pinpoint property returns in specific postal districts e.g., a WC1-index or E1-index in London, or for specific property types e.g., a 2-bed-condo-index vs a 3-bed-condo-index, while taking into account the implications of all transactions in the full dataset.
  • Automated Valuation:Technology and software play huge roles in properly estimating a propertys market value. Data science can leverage similar techniques to indexing, but instead, provide a more precise fair market value associated with a property one that is produced almost instantaneously and at little to no cost.
  • Geographic Information Systems (GIS):A geographic information system can help visualize and analyze intelligence based on ones location. There is a plethora of information that exists on this topic, such as public amenities, population by neighborhood, and more. A GIS can help reveal public transit options, prices based on location, commuting times, and even help find properties based on preset criteria.
  • Time Series Forecasts: Times series forecasting, much like predictive analytics, helps us determine where property markets are heading. Such intelligence on the market helps teams make better investments and deals, and produces higher financial returns. Data science methods can help make accurate predictions based on a set of data, determining future real estate property performance such as inflation, interest rates, GDP, and more.

Why You Need Commercial Real Estate Analytics Software

In order to stay on top of the latest commercial real estate data, owners and property teams need to know their options in terms of commercial real estate data providers. Below is a quick list in no particular order of commercial real estate data analytics companies that serve as a reliable source for any commercial property owners database:

  • CoStar Group:CoStar Group is a provider of information, analytics, and marketing services to the commercial property industry in the United States, Canada, the United Kingdom, France, Germany, and Spain.
  • Kaggle: Kaggle is a subsidiary of Google LLC, and is an online community of data scientists and machine learning practitioners. Real estate data is just a fraction of the data sets they can provide.
  • Reonomy:Reonomy leverages big data, partnerships, and machine learning to connect the fragmented, disparate world of commercial real estate. By providing unparalleled access to property intelligence, Reonomy products empower individuals, teams, and companies to unlock insights and discover new opportunities.
  • CompStak: CompStak is the leading crowdsourced commercial real estate data platform. They handle millions of data points each week to create a comprehensive, robust CRE data set.

How to Conduct Your Own Real Estate Market Data Analysis

Though its possible to access third-party commercial real estate tools and resources to gain important commercial property information, we know its not always the more effective solution. Instead, placing commercial real estate market data analysis capabilities directly into the palms of owners and property teams can streamline processes and create efficiencies in a given portfolio. Just imagine: You can have your very own commercial property owners database or commercial tenant database, without needing to outsource any information or process.

In order to start collecting moremeaningful dataabout your assets and the best real estate analytics, we first need to discuss how CRE owners should think about their strategy for making more data-driven decisions. With HqO, you can access all the commercial real estate insights you need with the right technology partners who can place this valuable information directly into your hands. For CRE owners to find the metrics that best meet their goals, we suggest that they start looking for correlations between four key metric categories in order to increase their portfolios net operating income (NOI): outcomes, outputs, activities, and resources.

It starts with tuning in to the problems you want to solve most for your portfolio, and defining your desired outcomes. Once we define these outcomes, we can then use them to inform which metrics you need to look at such as the measurables you can leverage immediately (outputs), the strategies you enact to achieve your goals (activities), and your inputs (resources) and what other data you need to collect. Since not all metrics and data are equal, and some are better predictors of desired outcomes than others, the data you gather should help answer questions to take actiontoday, while still getting you closer to your larger goals.

To achieve this, you need access to granular data to make more informed decisions around your buildings features and tenants. For example, to enhance your amenity offerings you may look at digital engagement (or clicks) with content/information on your amenities, tenants satisfaction with those amenities, mobile access data to those amenities, equipment sensor data associated with those amenities, class or service bookings, and so on. The more activations your building features have, the more data points you can combine to paint a more holistic picture of which amenities see the most traffic, which ones need improvement, and more showing you how your building is actually being used.

Sincedigital transformationis fairly new to CRE, finding meaningful metrics about your building can be few and far between and even more difficult to organize. Luckily, theHqOS end-to-end operating system provides CRE owners with a leap in the right direction. The three layers of HqOS which include ourMarketplace,Tenant Experience Platform, and theDigital Grid work seamlessly together to produce compounding positive results for any portfolio.

Our growing Marketplace of best-in-class technology partners which can be accessed through the Tenant Experience Platform enables landlords to activate more of their building features, while simultaneously providing tenants with a better experience and property managers with a higher volume of rich data. The Digital Grid then takes these activations a step further, serving as a connected and streamlined analytics offering that can collect tenant behavior, amenities, technologies, and building data all in a single location. By centralizing and structuring data within our CRE-specific data model, it helps owners and operators uncover insights, take action to differentiate their assets, and make intelligent decisions across their portfolio. It can also helpbenchmark your buildings performance against others to accelerate best-in-class experiences for any tenant.

For more information about HqOS and its data and analytics capabilities for commercial office teams, schedule afree demotoday.


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