How Does Machine Learning Help In Business Transformation?
The widespread use of cloud computing, the increase of compute power, and the invention of machine learning (ML) solutions that...
Alternative data is reshaping the way organizations understand, operate, and use data. Across industries, data-powered solutions increase performance by leveraging orthogonal data. Over the last five years, these technological investments have been yielding large returns. The alternative data market has passed its infancy years and is now seeing a lot of activity: technological progress, financial investments, and increased competitiveness among data providers. Such a dynamic ecosystem makes it easy for all organizations to leverage alternative data.
First up, Blue Orange Digital, a top-ranked AI development and data transformation company in NYC, will guide us through a look at how enrichment with third party data is increasing commercial real estate ROI. As technology is being taken to new levels with the incorporation of consumer data, social media, google trends, sentiment, and more. Exploring what alternative data actually is and how it can be put to use.
Later on, we’ll look at how to find diamonds in the rough and increase ROI in commercial real estate in Diversified Data: Identify Undervalued Properties With Alternative Data Sources.
Commercial Real Estate(CRE) applications can benefit from alternative data since it complements traditional data sources in many ways. Firstly, it accounts for the gaps and inconsistencies in an agency’s own internal data. Such data may be incomplete and lacking perspective, offering limited possibilities for analytics at scale. Secondly, alternative data enables applications that are otherwise unachievable by relying on traditional data sources, thus opening up a whole new world of opportunities. Data-powered services are leading innovation and create entirely new business models, all across the real-estate sector. Many existing CRE use cases demonstrate the powers of alternative data, as we will see later on.
What brings alternative data at the forefront of big data applications today is the maturity of data processing technologies as well as the ever-increasing volumes of data that become available over a variety of channels.
Let us iterate through some of the most common sources of alternative data.
Online traffic is rich in data that is potentially relevant to real-estate: information about potential buyers, house values, and neighborhood amenities can all be found online. Such information can be extracted from websites by means of automated scripts and turned into analyzable data points. Examples of data points that can be obtained by scraping the web include, but are not limited to the following: distance to popular locations, public transit accessibility, as well as restaurant reviews.
CRE analysis tools can leverage such web collected data as part of their evaluation models for properties and entire neighborhoods. They provide an understanding of local dynamics and help model an understanding of the local market. Like this, web-scraped data is filling an important gap and can help shape informed decisions with regards to real estate investments.
Credit card transactions, POS systems, and cash registers all record a variety of financial data: how much, how often, and on what money is being spent. Such data can be collected from both physical and electronic documents. OCR applications make it possible to create digital copies of receipts, invoices, and documents and turn them into structured, analyzable data.
Real-estate has a range of applications where financial transaction data can be used: from marketing campaigns to automated underwriting, and loan score approval. Moreover, when collected and analyzed over extensive time periods, financial data gives an insight into individual consumer behavior. This can help better understand different market segments and their evolution over time.
News segments, social media feeds, and online reviews constitute another important source of alternative data. They can be processed by NLP tools and algorithms then mapped to evaluations, attitudes, and emotions. Like this, it becomes possible to identify patterns between text contents and real estate decisions of potential customers.
Sentiment analysis data completes the inadequate perspective offered by traditional data sets. While it was known on a human level that real-estate investment decisions are not only backed by economic factors, technology has finally quantified some emotional factors as well. By understanding these emotional factors and mapping them to market events, sentiment data makes it possible to better understand real estate markets and the intricate relationships behind them.
A few applications powered by sentiment analysis are chatbots for customer service and automated tenant management. Similarly, NLP models with sentiment analysis capabilities are also at the core of modern property valuation models. From analyzing the sentiments of schools, malls, and emergency services, a property’s value can be affected by the businesses and services surrounding it.
Humans and their assets move a lot. Location data can offer answers to many questions: where they go, how much time they spend in a specific location, and what movement patterns they follow. GPS systems, mobile smartphones as well as WiFi networks are all designed to handle and collect location data. At the same time, websites, applications, and services are all leveraging location information, since it enables them to offer contextual experiences to their users. The ubiquity of location data has turned it into one of the most powerful sources of alternative data.
The real-estate industry is traditionally focused on “location, location, location.” It is no wonder that leaders in this sector heavily rely on geolocation data for understanding investment risks and opportunities. Location analytics enable a wide range of applications: from real estate planning and development tools to surveillance, tracking, and advertising. With increased access to cutting edge technology, real-estate agencies are now capable of using complex geographic and demographic data to their advantage.
The main job that sensors have is to collect data from their environment. This makes them a powerful source of data, and, you guessed it, alternative data. While many sensor applications are built around a specific data type (e.g. temperature monitoring), sensor platforms are increasingly modern and capable of collecting a variety of sensory information. Structured and unstructured data is collected by increasingly capable platforms. These are able to aggregate, process, and exchange sensor data in real-time. This makes IoT data one of the prosperous sources of alternative data.
Commercial Real Estate owners and developers can leverage the rich IoT data ecosystems to their advantage. Whether it is collecting weather data, real-time traffic, or google tracking data; the availability of sensor data enables new applications that would otherwise be impossible using internal data sources alone. Such is the case of real-time threat detection and response surveillance systems. At the same time, the alternative IoT data is at the core of intelligence and interconnectedness of smart buildings.
Alternative data sources are nowadays ubiquitous and leveraged across industries well beyond real-estate. Many factors contribute to their popularity. Firstly, modern data stacks are built to integrate a variety of data sources. This enables agile development when building predictive solutions since external data sets can easily be added or removed “on the fly”. Secondly, the alternative data market is seeing increased growth, since more and more companies are willing to monetize their own internal data and acquire data from third parties. This shows how alternative data sets are indispensable to modern machine learning and data science projects.
In the real estate sector, such a dynamic ecosystem lowers the entry barrier for new data-powered applications and services. As we have seen above, new use cases arise when different data sources are combined and leveraged for one common goal. These innovations can have an impact on all real-estate players, from investors, lenders, and agents to prospective customers. With more and more players joining the alternative data market with innovative data-powered solutions, disruption of the real-estate sector is inevitable.
To go into more depth on how alternative data can be used and implemented check out how Blue Orange Digital was able to utilize third-party data to effectively Identify Undervalued Properties with Alternative Data Sources.
When you hear about the power of alternative data to boost the bottom line in real estate, you might ask your IT team to work on it. However, implementing advanced algorithms, scaling a digital transformation, and capitalizing on all the available alternative data may not be within your team’s capability nor within your budget to experiment with. That means that an in-house solution is unlikely to work. Reach out to a top-ranked software development agency, like Blue Orange Digital, to discuss how they can deliver your real estate solution in 90 days or less.
Do you have any related questions? From real estate to health care and energy, the Blue Orange Digital team has extensive experience developing machine learning algorithms, analytic models, and custom big data solutions.
Tell us about your project today, schedule 15 minutes below to discover the power in your data.
Continue Reading... Part 2: Diversified Data: Identify Undervalued Properties With Alternative Data Sources.
Josh Miramant is the CEO and founder of Blue Orange Digital, a data science and machine learning agency with offices in New York City and Washington DC. Miramant is a popular speaker, futurist, and a strategic business & technology advisor to enterprise companies and startups. He is a serial entrepreneur and software engineer that has built and scaled 3 startups. He helps organizations optimize and automate their businesses, implement data-driven analytic techniques, and understand the implications of new technologies such as artificial intelligence, big data, and the Internet of Things.
All images sourced from: Canva