Introduction
“Real Estate gets its real chase from Big Data Analytics.”
For the long years, the real estate industry was completely driven by market speculations, gut feelings, and educated assumptions. It resulted in risks to many and profits to few. So, to combat better with growing industrial risk factors Data Science indices and analytics were brought to use. The blend of data science and predictive analytics in the real estate industry is making the business work with actionable insights. Various organizations and start-ups are tapping the significance of data-driven tools. As a result, implementation of the same brings better returns on investments and endless business opportunities.
What is Real Estate Data Science?
It is a comprehensive process of analyzing raw data through AI-driven algorithms and deriving relative information through them. Such data is collected from government databases, survey reports, case studies, and largely online sources in recent times. Thus, Data Science analysis helps to optimize market campaigns, accelerate revenue and enhance operational efficiency for real estate investors.
Descriptive data science focuses on identifying the real estate trends and diagnostic analysis considers the reason behind such changing trends. Furthermore, with predictive analytics investors and the company can forecast the upcoming trend followed by the establishment of the strategic plan. The concept focuses on creating power-packed applications in the real-estate industries by illuminating the following benefits.
- Testing the pressure expectations of individual property with a higher growth rate.
- Identification of assets holding greater value in near future.
- Facilitating capital expenditure decisions.
- Comparing predictive modeling outputs to establish an accurate forecast.
Use Cases of Data Science in Real Estate
The changing market scenarios in the real estate industry have pushed the need for accurate predictions to bridge the speculation gap. Evidently, the industry is no more a gimmick ground but a goal-oriented process of creating better profits. So, let’s understand how the data science concept can boost the real estate concept.
| Use Case | Description |
| Location Analysis | Using the GIS mapping software investors and the company can easily understand and analyze the locality intelligence much effectively. Hence, the location analysis has a tremendous effect on the preferred position of the residence and investment requirements. Ultimately, it influences the purchasing and selling behavior of the person. |
| Competitive Analysis | The real estate companies face rigorous competition every now and then which makes it necessary to ace every dynamic of real estate. And the strength is derived from data science and analytics algorithms which help to examine the rivals’ performance and establish strong strategies accordingly. |
| Facilitate Property Valuation | Generally, the process of property valuation takes intensive time and resources which demands huge human involvement too. But no worries because data science allows speedy and accurate calculation for the market value of the property. The valuation so determined is highly reliable, accurate, and less time-consuming. |
| Investment Indices | Investors often make decisions based on the future potential of the investment. Therefore accurate prediction and understanding of the upcoming scenario is the crucial need of the real estate industry. The scientific approach constructs granular sub- market indices regarding property demographics and characteristics. It leads to healthy and logical decision-making. |
| Leveraging frequent appraisals | In the real estate market appraisal rate determines the buying and selling behavior of the individuals. Thus, the appraisal shall be completely precise and accurate to ensure a healthy comparative study of the properties leading to the determined offer price with ease. |
| Tenant targeting | In the case of rental property, the choice and consistency of the tenant are a must and Data Science helps to determine the demand for rental units in the forthcoming years. Based on the prediction marketing campaigns and advertising targets are established to ensure the right tenant targeting. |
| Commercial Trends | Inarguably, commercial real estate investments are comparatively larger and complex than residential investments. Hence, the former requires an in-depth understanding of each real-estate data through a data science mechanism to establish the right analysis for the commercial trends. |
Data Science Metrics for Real Estate Industry
Besides development staff, the real estate industry revolves between two major role players’ investors and the agents. Thus, the major focus of Data Science and analytics lies in these two core industrial segments.
Real Estate Investor Metrics
- Payback Period
- Return On Investment ( ROI)
- Average Rent Price Per Property
- Operating Expense Ratio
- Loan to Value ( LTV)
- Tenant Turnover
- Average Mortgage Rate
Real Estate Agent Metrics
- Average Commission per Sale
- Listing to Meeting Ratio
- Number of Properties Advertised per Real Estate Agent
- Client Feedback Ratings
- Number of Days in Market
- Year-over-Year (YoY) Variance of Average Sold Price
- Sold Homes per Available Inventory
Top Data Science Real Estate Companies
The real estate domain deals with a vast amount of data requiring a proper structure to create an informative ocean of deep insights. Besides the huge volume of data, the real estate industry faces other challenges like immense manual contribution, reliability issues, fraudulent activities, location inaccuracy, and blindfold attitude of investment. As a result, technical market players came into the role to support the scientific journey of real estate industries.
| Company Name | Data Science Implementation |
| Placense | The company provides detailed and reliable data about customer behavioral statistics relating to a specific location. As a result, it eliminates the requirement for additional sensor installation by being the strong link between asset location and client location. |
| Startodem | Here, the data science models consider economic and hyper-local demographics to create the environment for healthy decision-making. The company deploys rigorous machine learning models to identify new patterns that may lead to some potential threats or opportunities for the industry. |
| Real Capital Analytics | The exclusive research process of the company focuses on the commercial real estate sector where property transactions across all markets are tracked. As a result, the companies can easily access the profiles of over 200k investors who are responsible for $20 trillion worth of transactions. |
| Cherre | A New York-based company is an emerging leader in data analytics that provides deep insight into the market scenarios. The company provides assistance in better investment, underwriting decisions, management, etc. Moreover, it helps to evaluate the real estate trends, asset class, benchmarks, threats, and opportunities by saving manual input costs to a significant extent. |
| Jupiter Intelligence | It is the global leader in data services and helps to mitigate risk factors such as sea-level rise, extreme weather, storm intensification, and a lot more. These factors play a crucial role in determining the location of the asset. Therefore, the real estate users plan, build and manage assets considering this vulnerability factor with the help of Jupiter Intelligence. The company integrates REIT structure with the climate perils and lower insurance costs. |
Final Words
Data Science has been a prominent solution to make the real estate industry relatively risk-free. It happens with the incorporation of essential indicators such as desirability analytics, price prediction, business cycle risks, etc. It has resulted in the paving way towards higher quality project management and a significant shift towards data-driven decision-making mechanisms. The major evolution in real estate has made it the investment hub and attractive field for the young generation. Thus, better the scientific approach, profitable are the results for the company, agents, and investors. Moreover, the data-driven prediction leaves no room for speculation which reduces the fraudulent activities and boosts the company’s brand identity. Some of the ingredients of the real estate market are already aware of data science potential yet a lot still needs to scale up to the parameter to sustain hiking competition rate.
Topics in Data Science
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