Top 10 Yardi Data Issues (and How to Fix Them)

Real estate companies thrive by using their data to make better decisions. But what if their data is not reliable? Here are the top 10 Yardi data issues (and how to fix them).

Leasing

1. Missing or Bad “Lease To” Dates

“Lease To” dates are when a tenant’s lease ends. Although this date can change, CRE firms improve planning by having accurate data. “Lease To” dates drive vacancy projections and marketing for renewals. Common issues include:

Blank “Lease To” dates
Incorrect “Lease To” dates, i.e. earlier than a “Lease From” date or after the “Move-Out” date
VERY incorrect “Lease To” dates way into the future, i.e. used as a placeholder and never updated

2. Missing or Bad “Move-Out” Dates

“Move-Out” dates are often optional fields. However, if a move-out date is incorrect, it will affect your team’s vacancy projections, risking immediate revenue loss. Blank “Move-Out” dates create a myriad of issues. They ruin the ability to calculate renewal rates and trade out spreads accurately.

3. Missing “Sign” Dates

“Sign” dates represent the date a tenant or resident signed their lease. Missing “Sign” dates are an easy catch and easy fix. Real estate firms use these dates to understand the lead to lease sales cycle.

4. Missing Rents

$0 rent or blank rent is rarely correct and easy to surface in a data governance analysis. If the unit is subsidized, the subsidy should be recorded in the subsidy section. If the unit is rented to an employee, then the rent should be entered with the matching special concession. For other special cases, the unit may have a “Down” unit status. Rents of $0 confuse users and negatively affect downstream reporting.

Accounting

5. Bad Accounting Trees

Accounting trees roll up financial transactions into appropriate financial statements. Often, the wrong code is in the wrong level, or the levels need updates. If accounting trees are inaccurate, real estate firms have inaccurate financial reports. This causes headaches and confusion for all firms involved.

6. Missing Charge Codes

Charge codes are needed when entering transactions into the accounting system. Blank or incorrect charge codes run the risk of bad collections reporting. They also reduce the ability to identify trends in collections. We recommend cleaning this data before your month-end process. This reduces the workload during end-of-year audits.

Operations

7. Missing Beds and Baths

Missing beds and baths in a unit is an easy-to-catch and easy-to-fix data issue (and a common one). Unit types should be monitored and reviewed for accurate setup. Incorrect beds and baths lead to issues in leasing, unit turns, and forecasting. Units should always represent the correct beds and baths for a certain unit type.

8. Missing SQFT or Area

The amount of square feet (SQFT) or Area drives rents and exposure. This data may be stored in a few different places, i.e. unit or property. Area data may also be mismatched in different geographies or property types. For example, one region or property type uses the property total vs. the sum of each unit. Agree to a methodology within your firm. Then, correct and maintain this data religiously.

9. Unmaintained Property Trees

Property trees dynamically manage and maintain a grouping of properties. These lists are often used to cross-filter reports and analytics by portfolio. Keeping these trees up to date is a manual task. Agree to the minimum number of property trees you will use. Be sure not to overproduce trees and require heavy maintenance.

10. Invalid or Missing Ownership Data

Ownership data is usually optional in property management systems. Entered correctly, it can assist with powerful reporting and analytics. Ownership data allows you to record and store ownership percentages over time. You may use ownership data to report using your owners’ specific ownership percentage. This allows you to paint a clearer picture of what matters to owners.

Conclusion

The best way to clean your data is to never let it get dirty. Implement a data governance report that surfaces bad data. Then, your will have data cleaner and shinier than Mr. Clean’s forehead! Check out our data governance dashboard to see how we help customers keep their data clean and crush their annual goals.

Top 10 Yardi Data Issues (and How to Fix Them)