Manoj Sigdel LeaseLock Rental Housing Data Q&A

Unleashing the Power of Rental Housing Data: Meet Manoj Sigdel, the Analytics Wizard Leading LeaseLock’s Tech Success

Manoj Sigdel, Head of Data and Analytics at LeaseLock, is a highly experienced technology leader with a focus on SaaS-based business intelligence (BI) products. He has over 15 years of experience in various industries, including real estate, property management, financial services, and insurance. Manoj holds an MBA in Strategic Leadership and possesses a deep understanding of emerging technologies, particularly artificial intelligence (AI) and machine learning.

Throughout his career, Manoj has successfully grown and led global BI organizations at companies like RealPage, Blue Cross Blue Shield, and Veritis Group, Inc. He has demonstrated a strong ability to deliver business value and drive revenue growth through BI product development, advanced data analytics, real-time reporting, and robust data visualization. Additionally, he has extensive expertise in launching, modernizing, and scaling enterprise database solutions.

At LeaseLock, Manoj’s proficiency in emerging technologies and his track record of growing global BI organizations have played a pivotal role in improving team and customer experiences. His contributions have been invaluable in LeaseLock’s mission to revolutionize loss protection and optimize asset performance for property owners and operators

LeaseLock has entrusted Manoj to optimize their AI technology and data platform as they continue to refine their core lease insurance platform. His interview below provides insights into the latest multifamily trends, challenges, opportunities, and how their AI technology platform enables rental housing operators to leverage data effectively.

Manoj SIgdel Head of Data & Analytics LeaseLock

Q&A With LeaseLock Head of Data & Analytics, Manoj Sigdel

1. The era of rent growth is over — what are some of the game-changing applications of data and AI technology to create more efficient, valuable properties?

Manoj Sigdel:
In the face of slow rent growth, rental housing operators must adapt proactively to the market. AI and machine learning offer valuable tools for analyzing data on renter behavior, maintenance costs, and other factors. This enables operators to identify areas for improvement and optimize their operations, leading to reduced costs and an enhanced renter experience.

AI-powered chatbots and virtual assistants further contribute to a better renter experience and higher resident retention by offering efficient customer service and personalized recommendations for amenities based on renter preferences. Predictive maintenance is another game-changer, lowering maintenance costs, improving resident satisfaction, and extending equipment lifespan. An even more powerful application of AI is using data to predict resident renewals allowing property teams to strategically offer incentives to increase retention.

2. Multifamily is discovering new and smarter avenues to manage risk to drive revenue. What role do data and AI play in empowering rental housing operators to better manage risk and generate asset value, and what factors determine whether a technology solution is effective in achieving both?

MS: Data and AI empower operators to make informed decisions, minimizing risk and maximizing revenue in rental housing. One significant benefit is the ability to predict and prevent issues proactively. For instance, LeaseLock’s AI-driven risk models forecast rent loss and damages for each property, generating insurance policies that effectively reduce bad debts within pricing constraints. These models are trained with ledger data, property characteristics, historical claim behaviors, and geographical attributes.

Data and AI play a crucial role in revenue management and risk management. The effectiveness of a technology solution depends on factors such as data quality, integration with existing systems, ease of use, cost, and scalability. Platforms like LeaseLock, built on historical data, offer significant advantages. Careful evaluation of these factors allows rental housing operators to choose the most beneficial tech solutions for their business

3. As an industry leader in overseeing data and analytics at an innovative technology company, what other areas of property management do you think are ripe for data-driven optimization?

MS: Data-driven optimization benefits various property management areas: maintenance, resident screening, energy consumption, and lease data. Historical maintenance data identifies patterns, predicting and preventing issues, optimizing schedules, and cutting costs.

Using data and analytics for resident screening minimizes defaults and evictions by identifying reliable renters based on credit, rental history, and employment. Energy usage data reveals cost-saving opportunities, while lease data analysis reduces turnover and maximizes revenue. These strategies will enhance asset value and mitigate risk for operators.

4. Rent optimization made a huge impact on the way multifamily approached pricing and revenue opportunities. But many owners and operators don’t take that same data discipline to the way they manage outstanding account balances on the backend. In today’s economic environment, what strategies should owners and operators lean into to optimize asset performance across the entire property revenue stream, and why are they so important?

MS: Amid today’s financial risks and the economic environment, optimizing property revenue is crucial. A data-driven approach to managing outstanding account balances is gaining popularity among operators.

By analyzing renter payment history, economic indicators, and relevant factors, targeted collections strategies prioritize high-risk accounts and absorb losses. Forecasting future renter defaults allows proactive risk mitigation. LeaseLock’s underwriting risk model predicts and optimizes coverage, helping properties recapture revenue instead of writing it off as bad debt.

5. Looking ahead, what do you think is the biggest opportunity for the multifamily industry? Are there any new generative and predictive AI trends you’re looking forward to most, or promising technology solutions the industry should embrace?

MS: Technology remains a significant opportunity in leasing, offering boundless possibilities to address various challenges for rental housing providers and renters. Innovations such as smart home systems and mobile apps enhance resident satisfaction and retention by enabling control, communication, and access to amenities.

Applying data and AI can greatly improve property operations and boost asset value. With access to abundant data, leveraging AI leads to better decision-making for firms.

Natural language processing (NLP) is another emerging data and AI trend that allows computers to understand and analyze human language. For property teams, NLP can improve communication with residents, automate customer service, and enhance decision-making processes. There’s also potential for computer vision–with the use of cameras and sensors to collect data to automate tasks like property inspections and maintenance.

6. Data science problems run the gamut from simple to complex. What has been the biggest hurdle—and most rewarding—to overcome while building LeaseLock’s AI risk engine?

MS: LeaseLock’s proprietary AI risk engine predicts property-level risk using ledger data, property characteristics, historical claim behaviors, and geographical attributes. Building such a powerful engine requires data science expertise and a deep understanding of multifamily industry challenges.

The quality of multifamily data poses a challenge, requiring large amounts of high-quality data to train AI models effectively. Additionally, AI models need constant adaptation to reflect changing risk factors, leading to ongoing data analysis and model refinement.

Despite challenges, LeaseLock’s team has engineered a leading AI risk platform for residential real estate, enabling clients to effectively mitigate risk, enhance financial performance, and provide better resident outcomes. This technology offers clients a significant competitive advantage.

7. What lessons have you learned along the way that you believe will help drive the next evolution in residential real estate?

MS: Since COVID-19, multifamily has embraced flexible approaches, recognizing the significance of virtual and remote operations and the need to prepare for unexpected disruptions. In my opinion, the reason some operators are better positioned to succeed in the future comes down to their ability to adapt and their willingness to leverage technology to optimize their business. .

As a technology leader, I’ve learned the value of data and analytics in driving better decision-making for our clients. AI and machine learning further enhance decision accuracy and reveal hidden patterns and trends. These lessons will propel the industry to drive better outcomes for residents, property teams, and owners in the future.

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