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  • Hot take: You might need machine learning, not AI

    Hot take: You might need machine learning, not AI
    2026/09/03
    Hot take: You might need machine learning, not AI“Machine learning is where I would start, specifically around questions around underwriting and valuations.”That’s Garret Van Parys on why real estate companies may be overlooking one of the most practical applications of data science while rushing toward AI.Garret Van Parys is the Chief Strategy and Analytics Officer of Esusu, a billion-dollar fintech platform turning rental payments into credit-building outcomes. He previously spent more than thirteen years at Invitation Homes, most recently as Senior Vice President, Enterprise Analytics & Revenue Management, overseeing $3 billion in annual revenue while helping change how residential real estate investment management uses data and analytics to drive performance, strategy, and operational efficiency.Garret helped lead the two largest mergers and integrations in single-family rental history and was recognized as an IMN "Rising Star Under 35." He holds graduate degrees from Duke University's Fuqua School of Business in Quantitative Management and an MBA from the University of Arizona's Eller College of Management.Jonas and Garret go back to the early days of single-family rental, when the industry was scaling rapidly but the systems and data infrastructure needed to run it simply did not exist. Their conversation moves from those early days of Excel and Access databases to machine learning, AI, revenue management, customer retention, and what real estate companies should actually be doing with these technologies today.In this episodeBuilding the data infrastructure from scratch. Garret takes Jonas back to 2012, when he joined Colony American Homes as roughly employee number 30. The company had an Access database and some Excel files, but no ERP or established property management systems. With the business acquiring homes at an extraordinary pace, the team had to build the systems needed to track acquisitions, renovations, inspections, and readiness from the ground up.The operational challenge of scaling single-family rental. Managing hundreds or thousands of individual homes is fundamentally different from managing a large multifamily property. Garret explains how the distributed nature of single-family rental created enormous operational complexity, with homes in different locations and different stages of rehab. Even knowing where every home was in the process could become a major challenge.How data helped make massive mergers possible. Garret was involved in both the Colony and Starwood merger and the later Invitation Homes merger. He explains how data and analytics became essential to answering practical questions about how to run a combined portfolio, including staffing levels, market overlap, operational efficiency, and organizational structure. The company went from zero homes to roughly 80,000 homes in about five years, making the ability to model and understand the business critical.Why machine learning deserves more attention. Garret's central argument is that real estate companies may be moving too quickly toward AI without fully exploiting machine learning. He describes machine learning as more deterministic, less expensive, more straightforward, and generally more explainable. Once the underlying data is in place, he believes machine learning should often be the first step for solving fundamental problems in areas such as underwriting and valuation.The technology is only as good as the people using it. Garret pushes back on the idea that an executive can simply open Claude or another AI tool and ask it to build a machine learning algorithm. The tools are increasingly accessible, but knowing whether an approach is scalable, durable, and actually solving the right problem still requires expertise. He compares these technologies to a paintbrush: having the tool does not automatically make someone a great artist.Why business knowledge matters as much as technical expertise. Garret believes the strongest analytics teams combine technical capability with deep knowledge of the business. The people building models need to understand the real-world problem they are trying to solve, while also being curious enough to identify problems executives may not have even considered. He argues that companies should focus on finding and retaining people who can bring those capabilities together.AI can uncover customer signals that humans could never monitor manually. Garret sees one of the most promising applications for AI in understanding customer interactions at scale. Recorded customer calls can be transcribed, analyzed for sentiment and topics, and monitored continuously. That creates a new ability to understand whether customers are satisfied, whether a representative handled an interaction well, and whether a customer may be at risk of leaving.Retention may be the biggest ROI opportunity in rental real estate. Garret connects customer experience directly to economics. Every additional renewal can ...
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    32 分
  • Before you make your next decision, ask AI this

    Before you make your next decision, ask AI this
    2026/08/27
    Title: Before you make your next decision, ask AI this"What is it we should be looking at? What is it we should know? What is it we need to do given what's in front of us?"That's Robert Salwasser on the question AI is forcing real estate owners and operators to ask.Robert is the founder and president of Income Property Specialists (IPS), where he has spent nearly four decades helping individual investors and family offices manage and grow their real estate portfolios. He co-founded IPS in the mid-1980s after studying economics and law, initially representing Bay Area landlords in property tax appeals and rent control hearings. He also developed an early web-based MLS for income property.Today, Robert leads IPS with a focus on combining first-class property management with investment profitability and cash flow. He also serves as a Multifamily Venture Partner at Shadow Ventures, sits on the Executive Council for Multi-Housing News, and serves on the Advisory Council for Blueprint.Robert and Jonas get into a central question for anyone managing or investing in property: what happens when AI can bring together all the information about a building and identify things you did not know to look for?In this episodeFrom property management to asset management. Robert explains how IPS is evolving beyond traditional property management into asset management, with AI providing a new ability to bring data together, ask questions, build models, and look for things the team may be missing. For Robert, the opportunity is not simply more technology. It is improving the odds of success for the owners and family offices they serve.AI is still in kindergarten. Robert describes today's AI as being in "kindergarten or first grade," while pointing to companies such as Palantir as being much further ahead. He believes the real transformation comes when AI can move beyond analysis and start understanding and interpreting information well enough to make decisions. He is interested in what happens when AI, computing power, and robotics develop together.Why AI could change the way you diagnose a vacancy. A vacant apartment does not necessarily mean the rent is too high. Robert describes how AI could look across rental data, tenant information, maintenance records, and other factors to identify the real reason a unit is sitting vacant. If maintenance is taking six days to complete work orders, for example, lowering the rent may be solving the wrong problem.The power of seeing the whole picture. Robert sees the greatest opportunity in bringing together information that traditionally sits in separate places. Leasing data, tenant longevity, rents, demographics, employment, maintenance, expenses, and other property information can all contribute to a much more complete picture. The challenge is making sure AI interprets that information correctly and combines it with the experience and judgment of people who understand the business.Exception management and missed revenue. One of Robert's most compelling examples is AI reviewing leases and rent ledgers to find missed opportunities. If a lease says a tenant has a pet and should be paying a pet fee, but that fee never appears on the ledger, AI can identify the discrepancy. Run that process regularly across thousands of units and small missed items can turn into meaningful improvements in revenue and net operating income.AI that tells you what needs attention. Robert does not want another report sitting in an inbox. He wants software that monitors the business, identifies something important, and alerts him. He gives the example of tracking an underlying interest rate index and receiving an alert a year in advance when a property's mortgage costs could become a problem. For him, the next step is AI that takes action when it sees something rather than simply producing paperwork.Why the right question matters more than the data. Robert returns to a lesson from a business class he took years ago: everyone wants data, but the real question is what that data is telling you. Forty years of experience can provide context that a number on a spreadsheet cannot. Robert's concern is that AI may have access to enormous amounts of information without yet understanding the context required to interpret it correctly.Robots are coming too. The conversation moves beyond software into robotics. Robert sees robots eventually handling repetitive physical tasks such as vacuuming hallways, landscaping, painting, and construction work. He points to Amazon warehouses that can operate without lights because robots are doing the work and argues that the implications for property operations and labor could be enormous.What AI can uncover that you do not know to look for. For Robert, one of the biggest benefits of AI is discovering things that would otherwise remain hidden. Instead of having people spend hours going through paperwork looking for anomalies and errors, AI can do the initial investigation. The ...
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    28 分
  • Will AI Recommend Your Apartment Community?

    Will AI Recommend Your Apartment Community?
    2026/08/20
    Title: Will AI Recommend Your Apartment Community?"I think we're optimizing to be understood and ultimately recommended."That's Sara Graham, talking about one of the biggest shifts AI is creating in multifamily marketing.Sara has spent more than 20 years in the multifamily industry, working at the intersection of marketing, technology, and operations. As the founder and CEO of Six Dots, she partners with operators and PropTech innovators as a growth strategist and trusted advisor, helping organizations simplify operations, strengthen their brands, and create scalable strategies that improve business performance and the resident experience.Sara has led marketing and customer experience functions on the operator side, and today works across both multifamily operators and technology companies, from emerging startups to established industry players. That gives her a unique perspective on how AI is changing not just marketing, but search, operations, reputation, resident experience, and the way apartment communities are discovered online.Jonas and Sara get into one of the most important questions facing the industry right now: in a world where AI increasingly answers questions on behalf of consumers, what does it take for an apartment community to be recommended?In this episodeFrom optimizing for clicks to optimizing for recommendations. Sara argues that multifamily marketing is moving beyond traditional search and advertising models. The new challenge is making sure apartment communities are understood by AI systems and surfaced as trusted recommendations when renters ask questions. That means thinking differently about content, data, reputation, and digital presence.Smarter marketing spend. AI is helping marketers make faster, more dynamic decisions about where to invest advertising dollars. Sara talks about how operators can move beyond static reporting and use AI to optimize campaigns, shift spend more quickly, and better understand what is driving leases rather than simply generating leads.Search is changing underneath us. Google is changing, AI search tools are emerging, and renters are increasingly interacting with recommendation engines rather than traditional search results. Sara explains why operators need to pay attention now, even though the exact future of search is still unfolding.Reputation becomes even more important. Reviews, ratings, and online sentiment have always mattered. In an AI-driven world, Sara sees reputation becoming even more critical because these signals help shape how communities are interpreted and recommended by large language models and search platforms.The data challenge. One of the biggest opportunities, and one of the biggest problems, is data quality. Sara discusses the importance of accurate property information, structured content, and making sure information about communities is consistent across platforms.AI as a force multiplier for marketing teams. Sara sees AI as a way to help marketers move faster and spend more time on strategy rather than repetitive work. Drafting content, analyzing performance, researching competitors, and building campaigns can all happen more efficiently, allowing teams to focus on higher-value work.Why operations and marketing are becoming inseparable. Marketing cannot promise an experience that operations cannot deliver. Sara explains why resident experience, service, communication, and reputation increasingly feed directly into marketing performance, and why AI may make those connections even more visible.AI agents and the future of apartment search. Jonas and Sara explore the possibility that renters may eventually rely on AI assistants to search for homes, compare communities, and narrow options. If that happens, operators will need to think differently about how their properties are presented to machines as well as people.What good marketing still looks like. Despite all the technology, Sara keeps coming back to fundamentals: understanding customers, delivering great experiences, and building trust. AI changes the tools, but the core principles remain remarkably consistent.Building at the intersection of marketing, technology, and operations. Through Six Dots, Sara works with operators and PropTech companies to help them navigate change, connect strategy with execution, and prepare for what comes next. Her perspective throughout the conversation is practical rather than theoretical: AI is already changing multifamily, and the companies that adapt early will have an advantage.Mentioned in this episodeSara Graham · Six Dots · Dwellsy · AI Search · Large Language Models (LLMs) · Reputation Management · Apartment Marketing · Resident Experience · Digital Advertising · Search Optimization · PropTech · Multifamily Operations · AI AgentsNet Effective is a conversation with the people running residential rentals about how they are actually using AI. New episodes weekly, about 30 minutes. Subscribe at neteffective.show.Connect ...
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    29 分
  • Treating AI like a junior: building trust in AI

    Treating AI like a junior: building trust in AI
    2026/08/13
    Treating AI like a junior: building trust in AI"AI is very exciting to everybody... it's opening up a lot of opportunities and it's somewhat leveling the playing field."That's Adam Siegel, Vice President of Product Growth at Crexi, talking about what he is seeing across commercial real estate.Adam has been at Crexi since August 2024, where he works on growing the company's intelligence platform by bringing better data onto the platform, acquiring relevant datasets and building workflows that help users get things done. Before Crexi, he spent more than 11 years at CBRE, managing the day-to-day operations of two of the country's largest capital markets teams, responsible for more than 250 deals a year and approximately $2–3 billion in annual consideration.Adam and Jonas get into what AI is actually changing for brokers, investors, researchers and other commercial real estate professionals — from faster market research and more granular data to AI-powered workflows, MCP servers and the risks of trusting AI with high-stakes decisions.In this episodeAI is leveling the playing field. AI is giving commercial real estate professionals faster access to data and allowing them to work at a velocity that was previously difficult to achieve. Adam talks about brokers, appraisers, lenders and private investors using AI to look at more deals, understand markets, check buy boxes and research things like market rents and new construction without spending hours digging through reports, emails and websites.Breaking down the data silos. Adam sees a major opportunity in connecting data that currently lives in different places. The goal isn't just to ask an AI a question and get an answer, but to let professionals interact with their data, drill down into it and keep asking better questions. He compares it to the evolution from canned reports to working directly with raw data in Excel and pivot tables.MCP: scam or the next step? Jonas brings up an industry contact who called MCP a "scam." Adam pushes back on the idea, arguing that MCP is further along the AI journey and becomes valuable for people who want to manipulate data, access it directly and keep drilling down rather than settle for a simple question-and-answer experience.The accuracy problem in high-stakes real estate. AI can move incredibly quickly, but Adam isn't ready to trust it completely with underwriting. He talks about the possibility of AI missing a critical clause, environmental issue or other detail in a transaction — potentially creating a multimillion-dollar mistake. The models are improving quickly, but the confidence level still needs to catch up.From AI chat to useful workflows. Adam thinks the next stage of adoption is about building AI into people's daily workflows rather than simply giving them another chatbot. Crexi is working on tools that can function as a research assistant, combine proprietary and user-provided data and let users drill down to the street level.Why commercial real estate needs its own AI. Adam argues that general-purpose models such as Claude don't have the same commercial real estate context and proprietary data that a specialised platform can bring. The opportunity is to tune AI agents specifically for the industry and give users insights they might not otherwise see.One dataset, multiple outputs. Adam talks about Crexi's Create product and the idea of using the same underlying information across the lifecycle of a deal — from a BOV and due diligence through to an offering memorandum, website and marketing. The objective is to save small amounts of time repeatedly, while also allowing the system to learn how an individual user works.AI and the future of multifamily. Adam believes multifamily is particularly well positioned to benefit from AI, including for smaller operators. He sees opportunities well beyond acquisitions and dispositions, including understanding rents, maintenance, property management and collections. He expects some asset classes to move faster than others, but believes every part of real estate will benefit from AI in some form.Building with AI instead of just talking about it. Adam is using AI to turn product ideas into working, clickable prototypes that can incorporate live Crexi data from Snowflake. Rather than spending hours explaining an idea to developers and designers, he can hand them something tangible that communicates his vision and gives them a head start.AI adoption at Crexi. Adam describes how Crexi's engineers, product managers, designers and leadership are increasingly using AI as a force multiplier. He says the company has taken a top-down approach to becoming AI-forward, with the ambition to be more than a marketplace and instead bring AI solutions to the industry.The callout: Crexi's engineering team. Every episode of Net Effective ends with a guest recognising somebody doing great work with AI. Rather than naming one individual, Adam gives the nod to Crexi's engineers, product ...
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    37 分
  • AI agents sound easy, but they aren't: inside real estate ops

    AI agents sound easy, but they aren't: inside real estate ops
    2026/08/06
    AI agents sound easy, but they aren't: inside real estate ops"Everybody underestimates how much work it takes to use AI, and then overestimates how much AI will do for you."That's James Goody, early in the first episode of Net Effective.James is a CPA and the Managing Partner of Oak 21 Real Estate, an advisory and execution partner (not a broker) working with family offices and operators across multifamily, single family and commercial. He is also CFO and COO of Coastal UP Partners, which runs two workforce housing funds. Before that he was CFO of two multibillion dollar real estate platforms, one of them backed by Goldman Sachs and Carlyle. He has been involved in the ownership and operation of more than $6 billion of real estate, including more than 40,000 apartment units. He has run IT at several companies and describes himself as a prop tech nerd. He is now using AI across accounting, utilities, renovation tracking and research.In this episodeFrom doing to reviewing. The clearest change AI has made to James's work. He compares it to standing up an outsourced team, where your own people move from producing the work to reviewing it. Pulling a rent roll, checking renovation status unit by unit and tallying spend used to be manual. That work is automated now, and what comes back is the outlier list showing which units are lagging.Connecting the systems. James is wiring QuickBooks Online, AppFolio and monday.com together to pull property insights he could not get before. He is candid that the break points between systems are where the trouble lives, and that connecting them is sometimes simple and sometimes anything but.Utilities and AP. James sits on the advisory board of LDGR Systems, which automates utility payments and accounts payable. A property manager he works with moved an accounting person off manual reconciliation entirely. That team now spends its time asking why utility costs moved in a given month and how that tracks against occupancy.The accuracy problem. AI returns an answer in seconds. James finds it pulling the wrong column, misreading what he asked for, or producing something that does not make sense. He and Jonas land on the same conclusion: AI output demands a different and often more demanding review than human work, because the mistakes are stranger.Claude, ChatGPT and the cost of switching. James started on ChatGPT and moved to Claude. He makes the case for using it as a deep research tool, explains why the context you build up inside one model makes switching genuinely hard, and gives his read on how Anthropic and OpenAI are each perceived right now.Whether AI replaces offshore teams. Jonas asks directly. James expects a combination of replacement and augmentation, and describes an AI company where outsourced workers wore recording headsets while they worked.Why service still has to be human. James tried to get help with a software plan and reached only AI agents, with no route to a person. Escalating produced the response "you seem upset." Both of them connect this to property management, where the product is somebody's home and bad service is not something a resident shrugs off. James makes the point that tenant retention is the name of the game right now, with rents flat and above market leases worth keeping in place.Privacy, security and who controls the data. As a former bank officer and national director of real estate at First Republic, James is cautious here. He gets into what happens to private company financials once they are uploaded, why he will not connect his personal Gmail to anything, and the current split between RealPage keeping data closed and Yardi going open. He frames that as Betamax versus VHS, with customers waiting to learn which format wins.AI agents, and the gap between easy and useful. Agents are what James most wants to learn next. Starting one takes seconds. Trusting the output, and deciding how much autonomy to hand over, is the hard part.AI in his personal life. He uses it about as much personally as professionally, including planning a trip to Healdsburg for a wedding. Neither he nor Jonas will let it book anything yet.The callout: Nick Virovec & Fischer Asset Management. Every episode of Net Effective ends with a guest naming an operator who deserves more credit than they get. James picked Nick Virovec, President of Fischer Asset Management in Cleveland. Nick is tech forward, piloted Ledger Systems, and used AI to reconcile a prior period adjustment across general ledgers, work that used to mean hours of Excel formulas.Mentioned in this episodeJames Goody, Oak 21 Real Estate · Coastal UP Partners · Nick Virovec, Fischer Asset Management · LDGR Systems · AppFolio · QuickBooks Online · monday.com · Claude · ChatGPT · RealPage · Yardi · First Republic BankNet Effective is a conversation with the people running residential rentals about how they are actually using AI. New episodes weekly, about 30 minutes. Subscribe at neteffective.show...
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    25 分
  • Net Effective Trailer

    Net Effective Trailer
    2026/07/10

    Trailer for Upcoming Season 1


    AI is changing how rental businesses run, and most of the conversation about it is still stuck on which tool to use. Net Effective: The Rental AI Show goes somewhere else.

    Hosted by Jonas Bordo, co-founder of Dwellsy and a 20-year veteran of the real estate space, this weekly series sits down with the owners, operators, proptech leaders, and advisers actually putting AI to work across multifamily and single family rental. They talk about what AI has saved them, where it's flopped, and what they're building next, all in 30 minutes or less.
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    1 分