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90% of decisions in real estate are data-driven. Today traditional, non-traditional and historical data significantly impact the real estate industry. It is projected that by 2025, 643 exabytes of data will be generated globally every day.

Property data has moved beyond just property details; changing valuation, appraisals, mortgage, lending rates, demographics, cost of living, facilities in the area, information on the neighborhood, and much more. Social media data predict consumer behavior and preferences, and market data is used to predict trends and impacts, and so on.

Top 5 Challenges in Aggregating Property Data

Data aggregators face multiple challenges aggregating vast volumes of property data from across the globe and presenting it in a usable format. Ensuring the relevancy, accuracy, and consistency of data adds to the challenge. The story will talk about five challenges and suggest the best possible solution to each challenge.

1. Managing multiple data sources

Property data is collected from multiple and disparate sources, structured, unstructured, online, offline, videos, documents, etc. The challenge is to integrate the entire data into a single standardized structure that meets the structure of client's database.

For instance, when you collect mortgage rates globally, the data would have different structures in the form of currency differences. Again, there are approximately 900 MLSs in the US with different data structures and ways of naming their fields. Aggregators will require to write a code for each type of data which is time and labor-intensive. Collecting data from different time zones would require scheduling bots and crawlers.

Real estate data collection experts organize your database with help of macros and bots supervised by human intelligence and deliver it in formats easily integrable into your applications like XML, CSV, JSON, etc.

2. Absence of data standardization

Data standardization is a prerequisite for the effective use of data in the entire chain of real estate data management. Gathered from multiple and disparate sources like listing sites, websites, databases, cloud storage, etc. in different formats, inconsistent data pose a challenge. Data is meaningful only if it is comparable to other datasets, which is not possible if data is not uniform.

Data standardization is the solution to this challenge which converts data into a uniform format helping with meaningful insights and optimal utilization of data by the real estate industry. Internally consistent data is an important starting point.

Manual standardizing is not feasible and even standardizing using excel has its limitations. It is through the application of intelligence and technology that the process can be accelerated without errors. Logical definitions, metadata, and labels can accurately standardize your data. Moreover, the increasing use of AI and ML makes deliverables more accurate in cost and time-effective ways.

3. Adopting right technology

Going by the reports, AI-based innovation will see 11.3 percent growth in German GDP by 2030 and will also touch the real estate industry. Real estate has been a little slow in adopting technology, but aggregators need to upgrade to stay in competition technology. Manual data collection can no more be used as it is error-prone, complex, labor-intensive, and not sustainable.

The answer is automation, but with technology advancing quickly, it is a challenge if you don't have the necessary skill and technology in place. You need to be educated on the kind of technology you would require for a particular need. Your approach to data aggregation would require elevating higher than essential tools. Technology and resources, if not optimized, can lead to revenue loss.

AI and Machine Learning can make collected data more actionable by drawing analytics from it, keeping in mind customer preferences. AI-based technologies also predict valuations and trends, leading to quick and easy transactions. The use of drones has also contributed to enhanced listings as they are designed for accurate data collection.

4. Creating a real-time data aggregation pipeline

Real-time data is crucial for strategic decisions in any industry, especially in the real estate business due to volatile and fluctuating markets. With fluctuating valuations, lending rates, rentals, and even customer preferences, it is of utmost importance to have a pipeline or system for gathering property data every second for accurate analytics and property intelligence.

There is a need for constant aggregating abilities, speed, and optimal storage. This is a challenge and can't be done manually. Searches need to be refreshed with automation where old data needs modification and replaced with the new one for analytics.

Live data streaming has its challenges, and the use of algorithms based on AI and Machine Learning assists in real-time analytics. Scheduled macros and bots across the globe with different time windows can constantly collect property data from other regions. Based on the requirement and sensitivity of data, scheduling can be done at intervals, continuously, or even generated by any event. And it doesn't end there; there has to be a process to constantly capture and process the data in line with the client's database. This calls for a skilled workforce, automation, and autoscaling to manage changing requirements.

5. Aligning aggregated data with site SEO

Data has no meaning if it doesn’t lead to high organic searches. Listings require constant optimization to increase the ranking and generate high leads. As a property aggregator, you can collect billions of data from multiple and diverse sources and integrate the data with the client's system. But whether the data you gathered is meeting the purpose. This is a specialized skill, and unless you have an SEO team to manage this part, this task is best outsourced.

SEO is not a one-time job; the data needs constant optimization so that the searches keep improving over time. It is an emerging technology getting advanced every single day and therefore the need for continuous up-gradation. Technology like NLP (Natural Language Processing), NLG (Natural Language Generation), TF*IDF (Term Frequency times Inverse Document Frequency), GPT-3 (Generative Pre-trained Transformer Number 3), automated on-page content and non-text optimization, etc. are used to keep the content optimized.

Conclusion

Considering the massive flow of property data from multiple sources, data aggregators have a considerable task. Aggregating data from numerous sources, keeping it clean and enriched is a challenge. Manual automation is no more an option. With AI and Machine Learning technologies gaining pace, it is essential to upgrade for that competitive edge.

Being a volatile and dynamic market, real estate needs constant feed with real-time data. And unless the content is SEO optimized again, it will make no sense for your client. The best option is to get access to technology and a skilled and scalable workforce and outsource such services to experts in the field. Outsourcing will prove cost-effective and fruitful.