Tenant Retention Optimization: Building an Interactive Power BI…

Limited Tenant Insights: HomeVibe Properties lacks actionable insights into tenant satisfaction and their concerns, making it challenging to proactively address issues.

Inefficient Lease Renewals: The lease renewal process is plagued by inefficiencies, contributing to tenant dissatisfaction and administrative overhead.

Lack of Trend Identification: Identifying trends and patterns affecting tenant retention is difficult, hindering the development of effective retention strategies.

Rationale for the Project

The importance of tenant retention in the real estate and property management industry cannot be overstated. Here are the top five reasons highlighting the significance of this project:

Enhanced Revenue: Improved tenant retention directly correlates with increased revenue and profitability as existing tenants renew their leases.

Positive Reputation: Elevated tenant satisfaction generates positive word-of-mouth, attracting new tenants and enhancing the company's brand image.

Operational Efficiency: Streamlining lease renewal processes reduces administrative overhead, allowing for better resource allocation.

Competitive Edge: Data-driven decision-making provides a competitive advantage in the highly competitive real estate market.

Sustainability: A focus on tenant retention aligns with sustainable business practices by reducing the environmental impact of turnover.

Aim of the Project

The primary objectives of this project are as follows:

Interactive Tenant Retention Dashboard: Design and implement an interactive tenant retention dashboard using Power BI.

Data Analysis: Analyze historical tenant data to identify factors affecting tenant churn and provide actionable insights.

Lease Renewal Optimization: Streamline lease renewal processes to increase tenant retention rates.

Data Description

This case study contains 4 datasets and they are as follows;

Tenant Information Dataset:

Tenant ID (N/A): A unique identifier for each tenant.

Tenant Name (Text): The name of the tenant.

Contact Details (Text - Phone number or email): Contact information for the tenant.

Lease Start Date (Date): The date when the tenant's lease agreement started.

Lease End Date (Date): The date when the tenant's lease agreement is scheduled to end.

Lease Details Dataset:

Lease ID (N/A): A unique identifier for each lease.

Lease Start Date (Date): The date when the lease agreement started.

Lease End Date (Date): The date when the lease agreement is scheduled to end.

Lease Term (Months) (Months): The duration of the lease in months.

Rent Amount (Currency - e.g., USD): The amount of rent for the lease.

Payment History (Currency - e.g., USD): The total amount paid for the lease.

Tenant Feedback Dataset:

Feedback ID (N/A): A unique identifier for each feedback entry.

Tenant ID (Link to Tenant Information Dataset): Identifies the tenant associated with the feedback.

Survey Response (Text): Text responses to survey questions, indicating tenant satisfaction.

Comments (Text): Open-ended comments provided by tenants, offering additional feedback or details.

Property Information Dataset:

Property ID (N/A): A unique identifier for each property.

Property Name (Text): The name or title of the property.

Location (Text - Address or coordinates): The location of the property.

Property Type (Text): The type of property (e.g., Apartment, Single-family home).

Amenities (Text): A list of amenities offered at the property.

Historical Occupancy Rate (%) (Percentage): The historical occupancy rate of the property, expressed as a percentage.

Tech Stack

Tool - Power BI It will be used for ;

Data Integration

Data Transformation

Data Analysis and Modeling

Real-time Data Visualization

Dashboard Design

Reporting

Cloud Integration

Project Scope

Exploratory Data Analysis: Explore the data to understand its characteristics and discover patterns.

Data Transformation: Prepare the data for analysis by transforming, encoding, or normalizing it.

Data Analysis: Analyze data to understand pattern in order to generate insights that will be visualized.

Data Visualization: Create visual representations to communicate insights effectively.

Interpretation and Insight Generation: Extract meaningful insights and interpret the results.

Data Analysis and transformation

Data Transformation

Combining Tables

The Tenant Information Dataset and Tenant Feedback Dataset were merged into a new table named Tenant Information and Feedback using Tenant ID as the key. This facilitated analyzing tenant satisfaction alongside tenant details.

Tenant Information and Feedback = 
NATURALINNERJOIN('Tenant Information Dataset', 'Tenant Feedback Dataset')

Renaming Tables

The Lease Details Dataset was renamed to Full Data to simplify referencing.

Creating Tables

I created two tables, Start Date and End Date, using the ADDCOLUMNS function to generate a comprehensive date range (2020–2025) and add metadata for time-based analysis.

Start Date = 
ADDCOLUMNS(
    CALENDAR("2020-01-01", "2025-12-31"),
    "Year", YEAR([Date]),
    "Month", MONTH([Date]),
    "Day", DAY([Date]),
    "MonthName", FORMAT ([Date],"MMMM"),
    "DayOfWeek", WEEKDAY([Date]),
    "DayName", FORMAT ([Date], "dddd")

End Date = 
ADDCOLUMNS(
    CALENDAR("2020-01-01", "2025-12-31"),
    "Year", YEAR([Date]),
    "Month", MONTH([Date]),
    "Day", DAY([Date]),
    "MonthName", FORMAT([Date], "MMMM"),
    "DayOfWeek", WEEKDAY([Date]),
    "DayName", FORMAT([Date], "dddd")
)

Creating a Measures Table

A Measures Table was introduced to organize all calculated measures.

Measures Table = DATATABLE(
    "DummyColumn", STRING,
    {{"DummyValue"}}
)

New Measures

Churn Rate

Calculated the proportion of leases that ended within the past year compared to total leases.

Churn Rate = 
CALCULATE(
    DIVIDE(
        COUNTROWS(FILTER('Full Data', 'Full Data'[Lease End Date] < TODAY() && 'Full Data'[Lease End Date] > TODAY() - 365)),
        COUNTROWS('Full Data')
    )
)

Total Expected Rent

Estimated the total expected rent revenue based on lease terms.

Total Expected Rent = 
SUMX('Full Data', 'Full Data'[Rent Amount (USD)] * 'Full Data'[Lease Term (Months)])

Rent Collection Performance

Assessed the ratio of total payments received to total expected rent.

Rent Collection Performance = 
DIVIDE([Total Payment History], [Total Expected Rent])

Current Occupancy Rate

Measured the current occupancy rate based on active leases.

Current Occupancy Rate = 
DIVIDE(
    CALCULATE(COUNTROWS('Full Data'), 'Full Data'[IsActiveLease] = 1),
    CALCULATE(COUNTROWS('Full Data'), ALL('Full Data'[Lease Start Date]), ALL('Full Data'[Lease End Date]))
)

New Columns

Satisfaction Score

A column was added to the Tenant Information and Feedback table to assign numeric values based on survey responses.

Satisfaction Score = SWITCH(
    TRUE(),
    'Tenant Information and Feedback'[Survey Response] = "Very Satisfied", 5,
    'Tenant Information and Feedback'[Survey Response] = "Satisfied", 4,
    'Tenant Information and Feedback'[Survey Response] = "Neutral", 3,
    'Tenant Information and Feedback'[Survey Response] = "Dissatisfied", 2,
    'Tenant Information and Feedback'[Survey Response] = "Very Dissatisfied", 1,
    0 // Default value
)

Historical Occupancy Rate Decimal

Added a column in the Property Information Dataset to convert the percentage occupancy rate into a decimal format for easier calculation.

Historical Occupancy Rate Decimal = 
'Property Information Dataset'[Historical Occupancy Rate (%)] / 100

IsActiveLease

Added a column in the Full Data table to flag active leases.

IsActiveLease = 
IF('Full Data'[Lease End Date] >= TODAY(), 1, 0)

Visualization Implementation in Power BI

After data transformation and the creation of DAX measures, the interactive dashboard was designed with the following features to ensure a user-friendly interface and actionable insights:

Card Visualizations

Purpose: Display key performance indicators (KPIs) at a glance.

KPIs being monitored

Churn Rate

Average Satisfaction Score

Average Occupancy Rate

Average rent

% rent rate

Detailed Visualization Overview

The dashboard features a variety of visualizations and interactive elements, tailored to analyze tenant retention and real estate performance effectively:

Line and Clustered Column Chart

Purpose:

Compare churn status and satisfaction scores across different property types.

Visualization Details:

Columns: Churn Status Line: Satisfaction Score X-axis: Property Type Y-axis (Primary): Satisfaction Score Y-axis (Secondary): Churn Rate (%)

Clustered Bar Chart

Purpose:

Break down satisfaction scores by survey responses to identify tenant sentiment trends.

Visualization Details:

Bars: Satisfaction Score Categories: Survey Responses X-axis: Survey Responses Y-axis: Count of Tenant Feedback Entries

Stacked Column Chart

Purpose:

Visualize the frequency of lease terms across various durations.

Visualization Details:

Columns: Lease Term Frequency Categories: Lease Terms X-axis: Lease Term (Months) Y-axis: Frequency (Number of Leases)

Donut Chart

Purpose:

Highlight the proportion of renewed vs. expired leases. Renewed Leases Expired Leases Values: Count of leases for each status Labels: Display percentages for clear insights.

Trend Lines

Purpose:

Showcase temporal patterns to monitor tenant retention and property performance metrics.

Trendlines Implemented:

Churn Status by Month:

X-axis: Month Y-axis: % of Churned Tenants

Renewed vs. Expired Leases by Month:

X-axis: Month Y-axis: Lease Status Count (Renewed, Expired)

Average Satisfaction Score by Month:

X-axis: Month Y-axis: Satisfaction Score (Average)

Rent Collection Performance by Month:

X-axis: Month Y-axis: Performance Metric

Drill-Through Buttons

Purpose:

Facilitate granular exploration of specific insights.

Drill Through Implementations:

Churn Rate Details:

Navigates to a page with tenant details contributing to churn.

Lease Term Details:

Detailed view of lease term distributions and their impact.

Satisfaction Score Details:

Displays detailed feedback and satisfaction trends.

Slicers

Purpose:

Provide dynamic filtering for enhanced analysis.

Slicers Used:

Month:

Filter data for specific months.

Year:

Toggle between years for year-over-year analysis.

Property Type:

Analyze specific property types.

Data Model

Tenant Retention Dashboard

Lease Term Details

Satisfaction Score Details

Churn Rate Details

Insights

Seasonal and Trend-Based Churn Patterns- the line chart tracking churn rates by month reveals clear seasonal patterns, highlighting specific times of the year when churn rates peak. These insights can guide targeted retention strategies during high-risk periods.

Lease Term Preferences and Their Impact on Retention- analysis of lease term frequency distributions shows how tenant preferences—whether for shorter or longer leases—correlate with retention rates. This insight suggests opportunities to adjust lease offerings to align with tenant behaviors and improve retention.

Direct Correlation Between Tenant Satisfaction and Retention- The bar chart comparing satisfaction scores by property type underscores the critical role tenant satisfaction plays in retention. Properties with higher satisfaction scores consistently exhibit lower churn rates, providing actionable data to reduce tenant turnover.

Financial Efficiency’s Impact on Property Performance- Insights into rent collection performance highlight operational strengths or weaknesses affecting the company’s financial health. Addressing these issues could improve the ability to invest in property enhancements and tenant services, boosting overall performance.

Effectiveness of Renewal Strategies Over Time- A longitudinal comparison of renewed versus expired leases measures the success of retention strategies. Trends can reveal whether recent efforts are effective or if adjustments are necessary to retain more tenants.

Recommendations

Implement Targeted Retention Programs- Leverage churn pattern insights to design focused retention initiatives during high-risk periods. Examples include personalized outreach, special promotions, or tenant engagement events to strengthen community ties.

Adjust Lease Terms to Align with Tenant Preferences- If data indicates a strong link between specific lease lengths and retention rates, offer flexible or preferred lease options to encourage longer tenancy, catering to tenant preferences.

Enhance Tenant Satisfaction- Focus on improving key areas impacting tenant satisfaction, such as property amenities, maintenance response times, and community-building activities. These efforts can directly address churn drivers and foster loyalty.

Optimize Financial Operations- Resolve rent collection inefficiencies by introducing technology-based payment solutions or incentives for timely payments. Improving these processes can enhance financial stability and fund tenant-focused initiatives.

Refine and Monitor Renewal Strategies- Continuously evaluate renewal versus expiration trends to measure the impact of retention

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Completed work

Posted Mar 26, 2025

primary project objectives: Design an interactive tenant retention dashboard Analyze historical tenant data Lease Renewal Optimization

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Timeline

Aug 10, 2024 - Sep 5, 2024