Data Annotation for ML Projects – 5 Most Effective Ways to Label Data
To remain competitive in a data driven market, real estate companies must reinvent and make a transformational shift towards automated process-backed data management.
The transition of the real estate industry from an investment focus to ensuring a safe customer experience spells an increasing reliance on data and data backed decisions as opposed to intuitive decision making. The need of the hour is to capture, process and analyze data from disparate real estate document sources, accurately and in real time, to generate actionable insights.
A third of the industry still uses manual workflows to manage title and deed data and struggles to resolve issues of property address mismatch, differing listing contract dates, mismatch in parcel numbers, etc. Manual document preparation, routing, processing, indexing and filing contribute to shockingly high data inaccuracies. Data silos are also huge red flags for the real estate data management sector.
Signed and scanned property title and deed agreement copies are stored as PDF files which weakens the quality of document images making it difficult to read. It causes optical character recognition (OCR) errors in identifying data fields, bringing in alien words.
Real estate documents carry handwritten remarks (often illegible) about critical terms in the property agreement. The style, and positioning vary. Also, handwritten notes can appear anywhere on the machine-printed contract page.
Handwritten remarks are the instructions about where handwriting should be used in the remaining printed text of the real estate agreement. Manual OCR struggles to identify handwritten characters from unstructured documents or a machine printed page. Often the position of the handwritten information while integrating it with adjoining text is misplaced. The accuracy, at which the handwriting is identified, varies.
Handwriting recognizing solutions are rare which work well with document OCR. These issues with automated data collection hamper the buying and selling process; a bottle neck even for realtors and real estate agencies.
Fix simple data entry errors before they cause big business losses.
American online real estate companies like Zillow and LoopNet empower their consumers with accurate database of more than 110 million U.S. homes through more than two dozen apps. The success of their business is defined not by the sheer volumes of data they manage but the accuracy of that data. So how do these and other such companies ensure data integrity and accuracy?
Real estate companies leverage a fine blend of standardized and robust processes, automated data entry workflows and a number of tools and software for electronic data processing to ensure higher accuracy of captured data. Automating data capture from scanned property documents and validating it eliminates data errors, improves customer satisfaction, and propels data governance and compliance.
Let’s have a look at the various stages of the data capture process and the multiple combinations of process standardization and automation deployed to ensure data accuracy.
The automated data capture process starts with receiving scanned document images through emails or FTP servers. Robots constantly monitor a dedicated folder on the FTP, where scanned images of title and deed documents are saved. Once the robot detects the presence of an image in the folder, it is picked, classified on the basis of pre-defined physical attributes and then routed for data extraction. Similarly, bots can be programmed to classify e-mail communication and open attachments to identify and route the property documents for processing.
Rule-based document classification and techniques helps to monitor influx, sort and route documents for accurate data capture. Bots programmed with ML algorithms compare physical attributes of a document with those stored in the image library for accurate comparison. This ability to classify the incoming document images reduces the need to manually sort the documents before processing, and ultimately reduces chances of errors and delays. “Reading” the entire document manually can be taken up in case of discrepancies.
Initially, using roadmaps with “structured zones” was the only option to automatically capture data from scanned document images. However, intelligent data capture technology makes it possible to process semi-structured or unstructured documents, while also checking single record, multiple history records of the same property, multiple history records of the same listing, multiple data sets (public records and listings) etc. effected though the location of data points in the document.
Instead of creating unique templates for each document type, templetised bots use defined data labels to automatically locate data on the document image – irrespective of where it is located. Technology automatically locates and captures data points such as zoning, tax amount, property type, land value etc. In the absence of such robust data management processes, Zillow could have failed to garner more than 36 million unique visitors a month.
Machine learning techniques
It is used to remember where the data was located, to ease up a task when next time that real estate document comes in for data capture and processing. Machine learning algorithms also alert the software to expect a certain type of data in a specific location. I.e., to ensure consistency, data point like date is indexed in a user- defined format i.e., MM/DD/YYYY, irrespective of how the date is mentioned on the document image.
Look up with unique and relevant data sources
Automated data capture workflows to cleanse data helps realtors to create unique listings that immediately syndicate to other listing websites such as Redfin, Trulia, and Hotpads. Automated solutions also look up the captured data with unique and relevant sources such as government and real estate websites. Using the information from these sources, it auto-populates and verifies data fields in a particular document. It enhances accuracy of data updation.
The accurate, verified data then can be transferred to client’s backend system according to their business practices. Completely automated data cleansing / transformation modules can be prepared using Python for quality control, comprehensiveness and accuracy. It is also used for getting the data ready for further analysis / reporting.
Exact match with existing business processes
Customized data capture solution providers use intelligent action bots, aka intelligent process automation (IPA). In their infancy, these bots learn how to handle unstructured and unclear data from data professionals. Such IPA bots are programmed to set rules to validate automatically captured data against their client’s business rules. Final format of the data, automatically captured, is customized to match existing business processes of the realtor.
Multi listing sites (MLS) like Zillow employ automated data entry workflows backed with tools and software, to adhere to the rules and procedures patterned on regulations published by the National Association of Realtors®. This way, their clients are no more required to change the way they use captured data to do business.
AI-based innovation will lead to 11.3 percent growth in GDP by 2030 and real estate industry will be affected alike, says PWC. AI has already announced its dominating presence to the industry. While many players have boarded the flight, others will be constrained to follow to remain competitive. So whether it is determining rentals for property, mapping your home needs to the exact property or determining interiors based on your specifications – bots enabled data entry solutions are calling the shots everywhere in the real estate landscape. AI’s capabilities to capture, compile, analyze and generate actionable insights promises to transform the way the real estate business operates.
Embracing automation and process excellence is an imperative for the real estate industry to ensure sustainability and customer engagement. There is a need to channelize efforts towards eliminating inefficiencies and increasing transparency. Rule-based, repetitive, and error-prone processes involved in buying, selling and leasing of properties need to be automated across processes – viewing homes, finding a mortgage, registering deeds or paying utility bills.
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