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How Data Entry Errors Cost Real Estate Companies Millions of Dollars

avoid real estate data entry errors
In the dynamic world of real estate, data serves as the backbone of informed decision-making, guiding everything from property valuations to market trend predictions. The accuracy of this data is paramount, as even minor discrepancies cost real estate companies millions of dollars and misinformed strategies.

Vast volumes of complex data and multilingual information make the real estate industry immune to data entry errors. These errors stem from simple human mistakes to complex system malfunctions.

Zillow’s home-buying algorithm disaster is a classic example of the costly impact of data entry errors. The median error resulted in Zillow purchasing 27,000 homes at prices that were not sustainable. Their algorithm, designed to make cash offers on properties, suffered from an error that made the company write off millions of dollars and slash its workforce.

In this blog, we will probe the cost of data entry errors in real estate, which can range from financial losses and legal disputes to misinformed decisions, compliance issues and a tarnished reputation. We will also discuss the solutions.

7 types of real estate data entry errors

Data entry errors are common in the real estate industry and can have significant impacts on business operations, revenue, and legal compliance. These errors can be broadly categorized into several types:

common types of real estate data entry errors
  1. Inaccurate Data Inputs: This includes entering wrong information, unintentional mistypes, or incorrect records. For example, a decimal point placed in the wrong position can lead to significant discrepancies.
  2. Wrong Data Formatting: This involves entering correct data in the wrong column or field or entering data information multiple times. For example, if a real estate agent enters a property’s square footage as ‘10’, it is unclear whether it is meant to be 10 square feet, 10 square meters, or acres.
  3. Transposition Errors: These occur when numbers or letters are swapped, such as entering improper payment amounts or incorrect phone numbers. For example, if a property’s listing price is $350,000 but is mistakenly entered as $305,000, it can lead to significant errors in the final calculation and overall property analysis.
  4. Representation/Unit Inconsistencies: This type of error typically happens when numbers are unintentionally mixed in with words, or when there are inconsistencies in measurement formats or units. For example, in the real estate business, if one property’s price is recorded as $300,000 while another is recorded as 300,000 Dollars.
  5. Data Misinterpretation: This occurs when data is misunderstood or misread, such as confusing a letter ‘O’ with the number ‘0’
  6. Missing Information: In real estate data, missing information such as incomplete property details, missing data, mislabeled data, and outliers can skew analyses and lead to poor decision-making. The median missing data rate for real estate variables can reach 47.22%.
  7. Inconsistencies due to changes in data: This happens when changes are made in the real data but are not accurately reflected across all relevant databases, leading to inconsistencies and errors. For example, in the real estate business, if one property’s price is recorded as $300,000 while another is recorded as 300,000 Dollars.

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Negative impact of data entry errors on the real estate business

Quantifying the impact of data entry errors in the real estate industry reveals a concerning picture, with significant financial, legal, and operational repercussions. While specific statistics can vary widely depending on the source and the market segment, several industry reports and analyses provide insight into the magnitude of these impacts:

  1. Lost Revenue: According to a study by IBM and the Data Warehousing Institute, businesses in the U.S. lose an estimated $600 billion annually due to data entry errors, including the real estate sector. In real estate, inaccuracies in property data can lead to underpricing or overpricing, with the former resulting in direct revenue loss and the latter potentially leading to prolonged listing periods and lost opportunities.
  2. Legal Fees and Disputes: Misreported property details can lead to legal disputes between buyers, sellers, and agents. The National Association of Realtors (NAR) has reported cases where disputes over inaccurate property listings have resulted in legal fees and settlements costing anywhere from thousands to millions of dollars, depending on the property’s value and the nature of the misinformation.
  3. Compliance Fines: Regulatory bodies enforce strict compliance standards in the real estate industry. Errors in reporting financial or property information can lead to fines and penalties. For instance, inaccuracies in the disclosure of property conditions or violations of environmental regulations have led to fines ranging from $10,000 to over $100,000.
  4. Operational Disruptions: A report by Gartner estimated that poor data quality costs organizations an average of $15 million per year in losses, which includes operational inefficiencies. In real estate, this could show as time wasted on verifying and correcting listings, delays in closing deals, and reduced client satisfaction.
  5. Reputational Damage: While harder to quantify, the reputational damage from data inaccuracies can lead to a loss of trust, which is crucial in the real estate industry. This can indirectly affect revenue and market position over time, as clients and partners may seek more reliable alternatives.

These statistics and reports highlight the critical importance of accurate data management in the real estate industry, highlighting the need for robust systems and processes to mitigate the risk of data entry errors.

Sources of real estate data entry errors

Data entry errors can creep into real estate documents through various means and processes. It could stem from typos, communication breakdowns, software glitches and data mishandling, undermining transaction accuracy.

Some common sources of data entry errors in real estate documents include:

common sources of data entry errors

5 real-life examples of real estate data entry errors

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.

Here are some real-life examples how data entry errors cost millions of dollars and tarnished reputation:

1. Incorrect Listing Price

Scenario: A data entry error made to the automated computing algorithm designed to make cash offers on properties.

Consequences: Data error resulted in Zillow purchasing 27,000 homes at prices that were not sustainable. The leading real estate company had to write off $569 million worth of homes and lay off 25% of its staff to compensate for the impact on its business.

2. Missing Information

Scenario: The amount of $793,595 was hidden in rows in the spreadsheets due to manual operating error while filing tax returns.

Consequences: A cashier was charged with first-degree theft, money laundering and 19 counts of filing false or fraudulent tax returns with the state Department of Revenue. He was accused of stealing the funds by exchanging checks for real estate excise taxes with money from the office’s cash drawer.

3. Transposition Errors

Scenario: Last-minute billing adjustments by City council staff mistakenly overcharged 3,000 customers.

Consequences: The total error resulted in net refunds of $16,276 to district customers through recalculated property tax bills. The number adjusted is relatively a very small number of underpayments processed for thousands of customers.

4. Incorrect Data

Scenario: $20 million theft from a top real estate firm and key witness died.

Consequences: Authorities announced arrests in the theft of $20 million from America’s largest commercial-real-estate company. The officials discovered only $4 million, rest of the money is missing and the lead witness in the case died.

5. Inaccurate Data

Scenario: County tax assessor’s office missed assessing 25,000 acres of oil fields.

Consequences: The mistake on a clerical error by an experienced staffer who used the wrong spreadsheet to calculate missed out on annual revenue worth $12 million.

These examples underscore the critical importance of accurate data entry in the real estate industry and the potentially devastating financial consequences of errors. Manual document preparation, routing, processing, indexing, and filing contribute to shockingly high data inaccuracies.

5 ways to address real estate data entry errors

To mitigate these types of data entry errors, real estate professionals often implement quality control measures, use specialized software, and provide training to personnel involved in data entry. Regular data validation and verification are also essential to catch and correct errors before they lead to significant problems in a transaction.

Real estate data entry can be a complex and detail-oriented task. Here are some techniques and examples to help ensure accurate and efficient real estate data entry:

  1. Use Data Entry Templates – Create standardized templates for different types of real estate data, such as property listings, client information, or rental agreements.

    Example – For property listings, create a template that includes fields for property type, address, square footage, number of bedrooms, bathrooms, and so on.

  2. Auto-Fill and Drop-Down Menus – Implement auto-fill and drop-down menus in your data entry software to minimize manual typing and ensure consistency.

    Example – When entering property locations, provide a drop-down menu of predefined neighborhoods or cities to choose from.

  3. Data Validation Rules – Set up data validation rules to restrict the type of information that can be entered into specific fields.

    Example – Validate phone numbers to ensure they follow a specific format, such as (123) 456-7890.

  4. Auto-Capitalization and Formatting – Set up rules for auto-capitalization and formatting to maintain consistency in data presentation.

    Example – Automatically capitalize the first letter of each word in property descriptions to ensure professional-looking listings.

  5. Error Checking and Alerts – use double -keying data entry method and implement error-checking algorithms to identify and flag data entry errors in real time.

    Example: If a user attempts to enter a property with an invalid ZIP code, the system can provide an alert and suggest corrections.

These techniques, when applied appropriately, can streamline real estate data entry processes, reduce errors, and improve the overall quality and accuracy of your real estate database. Choose the techniques that best fit your specific needs and workflows. Multiple techniques with macros and defined rules helped a listing site with accurate property data.


The impact of data entry errors in the real estate sector goes beyond numerical mistakes. These errors damage the essence of property transactions, leading to financial losses, legal complications, and damaged relationships and reputations. The need for accuracy in data management is crucial for the industry. Real estate companies should implement stringent quality control measures, leverage advanced technologies, and adopt a culture of accuracy to counter the adverse effects of data entry mistakes.

By doing so, they can enhance their financial resilience, ensure legal compliance, and most importantly, continue the trust and loyalty of their clients. In the real estate domain, value is not only measured in monetary terms but also in the accuracy and clarity of the data that drives the industry. For cost-effective and efficient solutions, companies must consider outsourcing real estate data entry tasks, which allows them to tap into specialized expertise, streamline operations, and focus on core real estate activities while minimizing the risk of data entry errors.

Author Chirag Shivalker
About Author:

 heads the digital content for Hitech BPO, an India based firm recognized for the leadership and ability to execute innovative approaches to data management. Hitech BPO delivers data solutions for all the aspects of enterprise data management; right from data collection to processing, reporting environments, and integrated analytics solutions.

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