Data Quality in Underwriting Alternative Real Estate Assets

4 Star Mid-Rise Apartments

Loan Analytics Database – Internal Report (December 2025)

Lenders in the U.S. real estate sector face mounting challenges when underwriting alternative property types – including rural housing, manufactured home communities, senior housing, and student housing. These niche assets, while increasingly attractive amid demographic shifts and affordability pressures, suffer from significant data quality gaps. This report-style analysis maps the current data shortcomings, examines lender underwriting challenges, proposes a standardized reporting schema for transparency, and provides a five-year outlook (with a focus on Midwestern rural trends). We also integrate insights from trusted external sources (Freddie Mac, Fannie Mae, HUD, Moody’s, ULI, NAHB, etc.) and include reference hooks to industry data providers and analytics platforms (Trepp, Reonomy, CoreLogic, RCA). The goal is to arm investors and lenders with a comprehensive, investor-grade perspective on improving data quality in these alternative real estate classes.

1. Mapping Data Gaps in Niche Property Types

Sparse Performance Benchmarks: Traditional real estate metrics (cap rates, NOI yields, default rates) are not well-publicized for alternative assets. Broad industry measures exist – for instance, the U.S. rental vacancy rate stood at ~7.0% in mid-2025 – but such averages mask local volatility. Rural rentals or specialized housing often deviate from national norms with little visibility. In fact, alternative sectors have quietly outperformed recently; manufactured housing, senior housing, self-storage, and student housing led all property types in one-year total returns. Yet, standardized benchmarks for these segments are scarce because data is fragmented. Unlike conventional apartments or offices (which benefit from widely tracked indices and reports), there is no ubiquitous index for, say, rural multifamily loan performance or student housing cap rates.

Limited Market Comparables: Lenders rely on comparable sales and leases to underwrite value and income, but “comps” are often hard to find for these niche assets. The overall transaction volume in such sectors is relatively low – e.g. U.S. student housing property sales totaled only about $5 billion in the first three quarters of 2025. By contrast, mainstream apartments saw roughly $35 billion change hands in just Q2 2025. This illustrates a stark data gap: fewer transactions mean fewer reference points for valuation. In rural areas, unique property features (large land parcels, lack of similar properties nearby) make traditional comp analysis unreliable. As a result, appraisals and underwriting often lean on broad assumptions or outdated info. Reonomy, a CRE data platform, notes that finding reliable data on mobile home parks “can be extremely difficult,” often requiring hyper-local knowledge and on-the-ground research. The same holds for other specialized segments – many deals are off-market or handled by local brokers, leaving big data providers with blind spots.

Incomplete Rent & Occupancy Histories: Unlike urban apartments where rent rolls and occupancy rates are tracked by services, alternative assets lack comprehensive historical datasets. Student housing is a prime example – occupancy is highly seasonal (peaking each fall) and dependent on university enrollment cycles, yet only specialized firms (RealPage, Yardi, etc.) collect this info. As of Fall 2025, student housing occupancy averaged around 91–96% nationally, indicating strong demand, but granular histories (e.g. pre-leasing rates by month, year-over-year rent growth per campus) are not broadly available to all lenders. Senior housing has NIC (National Investment Center) tracking primary-market stats – showing occupancy rebounded to 88.7% in Q3 2025, the 17th consecutive quarterly increase – but smaller markets and mom-and-pop elder care homes are untracked in the NIC database. Manufactured housing communities (MHCs) similarly report near-full occupancies; top REITs operate at 94–98% occupancy with robust NOI growth. However, those figures mostly come from public companies’ reports. The majority of MHCs are privately owned and don’t report occupancy or tenant turnover data to any central source. In rural single-family rentals or farm-town retail properties, rent collection and vacancy history might live only in the owner’s ledger. This lack of verified history makes it difficult for underwriters to model cash flow stability or stress scenarios.

Holes in Demographic & Demand Indicators: Alternative assets are often driven by specific demographic trends (e.g. an aging population driving senior housing, or remote workers driving rural housing demand). Yet, integrating such trends into underwriting is hard when data is spotty. For example, migration to lower-density areas surged during the pandemic – mortgage applications in rural communities jumped ~80% above pre-2020 norms. That demand has remained above historical levels even as urban housing cooled, with many buyers coming from outside the region. These insights (from Fannie Mae’s research) highlight rural growth pockets, but lenders don’t uniformly incorporate them due to lack of standardized data. Midwestern rural areas in particular may be underrepresented in national datasets. Some Midwest rural counties have quietly gained residents (often retirees or remote workers seeking affordability), but the absence of frequent Census updates or real-time population data is a gap. Moody’s Analytics notes that while the South saw the largest population gains since 2020, the Midwest’s growth has been comparatively flat. That suggests many Midwestern rural markets are stable at best – a critical nuance for underwriting – yet lenders must piece this together from disparate sources. In short, robust data on local economic drivers, migration, and demand for these property types is not readily packaged for underwriting purposes.

Bottom Line: Data gaps span performance benchmarks (few indices or averages), market comps (low transaction volume), rent/occupancy history (limited time-series data), and demand drivers (demographics often anecdotal). Lenders are often “flying blind,” or at least with blurred vision, when assessing these alternative assets. The next section explores how these gaps translate into real underwriting challenges for lenders.

2. Underwriting Challenges for Lenders

Higher Uncertainty and Risk Premiums: When data is poor, underwriting becomes as much art as science. Lenders compensate by conservatively structuring loans – requiring higher debt service coverage ratios, lower LTVs, or charging yield premiums – which can make financing costly or scarce for alternative asset borrowers. For example, in the student housing sub-sector, the lack of operating history on many newly built projects has historically led to mispriced risk. During the 2010s, student housing boomed with new developments, but many lacked long-term performance data. CMBS lenders stepped in to finance these deals, often on optimistic assumptions. The result: by 2019, student housing loans in CMBS had a 7.36% delinquency rate – far above the multifamily average – and numerous loans had debt service coverage below 1.0x even while current. This high default incidence was a direct consequence of underwriting in the dark. Lenders simply did not have robust data on how occupancy and rents would hold up when a shiny new rival opened down the road or when enrollment dipped. Such outcomes have made many lenders skittish, reinforcing a cycle where limited data leads to higher perceived risk, which in turn raises borrowing costs or limits credit availability for these assets.

Valuation and Appraisal Difficulties: In rural and other non-traditional properties, appraisals can be all over the map. Without comparable sales, appraisers must rely on income approaches or broad market proxies, which introduces subjectivity. Lenders then face uncertainty on true collateral value. A rural motel-turned-apartment in a county with declining population might appraise with a huge value range, depending on whether one assumes optimistic future occupancy or not. Manufactured home parks often consist of both real estate (land) and personal property (the homes), further complicating valuation – should the lender value only the land lease income, or also consider the homes if park-owned? Inconsistent approaches here make it hard to benchmark LTVs across lenders. Moreover, alternative assets might not fit neatly into standard property condition assessment templates, leading to overlooked capital expenditure needs (e.g. the cost to modernize an older assisted living facility or a 1970s mobile home park’s utilities). Lenders risk underestimating future CapEx and reserves when data on such costs is thin.

Cash Flow Volatility and Covenants: Many niche assets have inherently more volatile or seasonal cash flows. Student housing properties typically collect the bulk of rent during the academic year and often see occupancy plunge in summer months – a very different pattern from conventional apartments. Seniors housing and care facilities face move-in/out driven by health events or mortality, plus higher operating expense ratios; small changes in occupancy or labor costs can swing the NOI. Lenders, lacking industry-wide benchmarks, may struggle to set appropriate covenants or escrow requirements. For instance, what is a “normal” occupancy cushion for an assisted living facility? Mainstream multifamily might assume a 5% vacancy in underwriting, but senior properties historically had higher vacancy until the recent recovery (occupancy was in the low-80% range post-COVID and only rebounded to ~88% by late 2025). If a lender assumed 95% stabilized occupancy like an apartment, they could vastly misjudge revenue. Without better data, lenders err on the side of caution – potentially requiring sponsors to provide debt service reserves or extra guarantees to mitigate uncertainty.

Illiquidity and Exit Risk: An often underappreciated challenge is the exit strategy for the loan. Alternative assets can be hard to sell or refinance if things go wrong, precisely because the buyer pool is smaller and data transparency is lower. A lender foreclosing on a rural shopping center or a student housing complex in a tertiary market might face a thin market of buyers, prolonging the resolution. Rating agencies and bank examiners are cognizant of this liquidity risk. They frequently flag limited investor demand and scarce market data as reasons to assign stricter risk weights or lower credit ratings to loans backed by these assets. In other words, a loan on a generic suburban apartment might be considered less risky (all else equal) than a loan on a Midwestern rural senior housing project, not solely due to property performance, but due to uncertainty in exit value. This dynamic forces lenders to build in extra margin of safety – whether via higher interest spreads or lower loan proceeds – making deals tougher for borrowers to pencil.

Regulatory Scrutiny and Capital Allocation: Banks and CMBS lenders are subject to regulations that penalize opaque assets. Under Basel bank rules, loans without clear data might attract higher capital charges. Similarly, regulators post-2020 have focused on commercial real estate concentrations and could view an overexposure to niche segments as risky. Lenders therefore face internal limits on how much they can lend to, say, manufactured housing communities or assisted living, regardless of local opportunity. Several major banks have dedicated lending teams for seniors housing or MHCs, but they rely heavily on partnership with agencies (Fannie Mae, Freddie Mac) or specialists to get comfortable. Absent consistent data, loan approvals often hinge on subjective factors (sponsor experience, recourse strength) rather than asset fundamentals alone. This can disadvantage new or smaller borrowers in the alternative space and concentrates lending with a few experienced players. It’s a classic chicken-and-egg: better data would democratize credit availability, but until lending expands, the data remains siloed.

In summary, lenders underwriting these alternative real estate assets must grapple with higher uncertainty, tougher valuation exercises, the need for stricter covenants, liquidity worries, and regulatory capital implications – all traceable in part to poor data quality and transparency. The next section outlines a potential solution: an industry-standard data schema and reporting framework to bring these assets out of the shadows.

3. Toward an Industry-Standard Data Schema for Transparency

To address the transparency gap, the industry needs a unified data reporting schema for alternative real estate assets. This would mirror how standardized reporting exists for conventional assets (e.g., the MBA’s loan performance surveys or NCREIF indices for institutional properties), but tailored to the nuances of rural and niche housing assets. Key elements of this proposed framework include:

• Common Classification & Definitions: First, clearly define each asset subtype to ensure apples-to-apples comparisons. For example, delineate Manufactured Housing Community (MHC) as distinct from RV parks or mobile home subdivisions; define Senior Housing subcategories (independent living, assisted living, skilled nursing, active adult 55+) which have different performance profiles. Utilize existing frameworks such as HUD’s Urbanization Perception Small Area Index (UPSAI) to consistently classify rural vs. urban properties. A property’s “rural” designation could be standardized based on UPSAI or Census CBSA definitions, rather than subjective labeling. Similarly, a student housing property might be defined as one with >80% beds leased to students and located within X miles of a campus, etc. Agreeing on such definitions is foundational for any data schema.

• Standardized Performance Metrics: The schema would require collecting a set of core metrics for each asset on a regular basis. These should include occupancy rate (with nuance – e.g., for student housing, report both fall peak occupancy and annual average occupancy; for senior housing, report occupancy by unit and by available beds); rent roll details (average rent per unit or per bed, concessions given, year-over-year rent growth); operating cost ratios (operating expense as % of revenue, which is crucially higher in seniors housing due to staffing). Metrics like turnover rate (percentage of tenants leaving per year) and lease duration are also important – student housing might largely use 12-month leases aligned with academic years, whereas manufactured home parks often have very low turnover (tenants stay 5+ years on average, given the cost to move homes). By mandating these metrics, lenders and investors could start benchmarking what “normal” looks like for each asset class.

• History and Trends: It’s not enough to collect a current snapshot; the schema should encourage owners to provide historical time series (at least trailing 3-5 years of key metrics). This could be facilitated by a secure data repository (possibly managed by an industry consortium or a data vendor) where property managers upload quarterly figures. Over time, this builds an anonymized database. Trepp, for example, offers an alternative property performance database drawn from securitized loans, which tracks income and occupancy trends for things like student housing. Expanding such efforts beyond just securitized loans to all portfolios would greatly enhance trend visibility. The goal is to be able to answer questions like: “What is the typical seasonal occupancy swing for a 200-bed student apartment in the Midwest?” or “How did manufactured home community NOI perform during the last recession?” Currently, answers to those are anecdotal; a standardized data collection could make them empirical.

• Unique Data Fields per Asset Type: Each alternative asset has unique drivers that should be reported. A proposed schema would include fields like: for Student Housing – university enrollment trends, distance to campus, pre-leasing percentage by July 1 for fall term (a key indicator of leasing velocity), and perhaps the university’s Tier/rating (since a flagship state university market behaves differently than a small private college). For Senior Housing – include the share of units that are Medicaid/Medicare-reimbursed (for skilled nursing), the penetration rate of seniors in the local market (units per 1,000 seniors, often tracked by NIC), and caregiver staffing levels (since labor shortages directly impact performance). For Manufactured Housing – include the percentage of park-owned homes vs. resident-owned (this affects maintenance costs and turnover), and perhaps regulatory context (e.g., is the community subject to rent control regulations?). For Rural Properties (which could span various uses, from rural apartments to mixed-use buildings in small towns) – capture local economic indicators: population growth/decline, nearest metropolitan area, major employers in a 50-mile radius, etc. These contextual data points help flesh out risk factors that aren’t obvious from NOI numbers alone.

• Data Validation and Auditing: To ensure reliability, an industry body or third-party auditor could validate submitted data. Much like NCREIF data is contributed by fiduciaries and subject to standards, a Loan Analytics Consortium could be formed where lenders and owners submit their property data in exchange for access to industry benchmarks. Technology can aid here – for instance, APIs with property management software (used by operators) could auto-feed occupancy and rent data into the system, reducing manual errors. The dataset should also integrate public data (e.g., HUD’s data on FHA-insured multifamily defaults, or USDA data on rural housing programs) to cross-verify performance in these segments.

• Reporting Frequency and Transparency: Ideally, the reporting would be quarterly for larger assets and at least annually for smaller ones. Results could be aggregated and made available in anonymized form to subscribers (lenders, rating agencies, researchers). Think of an online dashboard where one could query, for example, “average occupancy of senior housing in Midwest versus Sunbelt” or “5-year rent growth trend for manufactured home parks in non-metro areas.” Rating agencies and regulators would likely welcome such transparency. Over time, this can even feed into credit models – Moody’s and S&P could refine their loss given default assumptions for these asset classes once they have richer historical datasets.

Establishing this schema and reporting structure will require industry collaboration. Trade groups like the Mortgage Bankers Association, Urban Land Institute (ULI), and specialty associations (e.g., the National Association of Home Builders for manufactured housing, NIC for senior housing, and NMHC’s Student Housing Council) can convene stakeholders to set the standards. The role of data vendors will be key – companies such as CoreLogic, Reonomy, Trepp, and Real Capital Analytics (RCA) are natural partners to host or distribute this data. For instance, CoreLogic’s vast property database could incorporate fields for rural and manufactured housing attributes, and Reonomy (known for covering off-market and secondary market properties) could help identify and track those assets that don’t appear in major metro-focused datasets. By providing “backbone” data, these vendors would also generate business from lenders hungry for insights. Indeed, several are already moving in this direction – Trepp has an Alternative Property data module, and NIC has extended its senior housing coverage to include “secondary markets” due to demand from financiers.

In summary, an industry-standard data schema and reporting framework would bring alternative assets into parity with mainstream sectors in terms of transparency. It would reduce uncertainty premiums by providing lenders and investors with hard numbers on performance benchmarks, thereby enabling more confident underwriting. While building this will take effort, the payoff is a more liquid and efficient market for these crucial but under-served property segments.

4. Five-Year Forward-Looking Insights and Macroeconomic Implications

Looking ahead, the next five years (2026–2030) will be pivotal for alternative real estate assets. Several macroeconomic and demographic trends are poised to shape performance, especially in Midwestern rural areas:

Persistent Housing Affordability Crisis: Affordability issues show no sign of abating, which bodes well for demand in alternative housing segments. As of 2025, an estimated 57% of U.S. households cannot afford a $300,000 home (the median price in many areas). This gap pushes more people to rent for longer, and to seek cheaper housing options. Manufactured homes and rural rentals stand to benefit, as they often represent the most affordable housing in their markets. The NAHB affordability stat implies that over half of potential households will be renters by necessity, not choice. The Midwest, with generally lower housing costs, might see inbound migration (or less outbound flight) as cost-sensitive families rethink locating in pricier regions. Midwestern rural towns could experience a modest renaissance in population as remote work and retirement drive households to seek lower-cost, low-congestion communities. We anticipate selective growth in areas offering a good quality of life and fiber-optic connectivity – think small Midwest college towns, or scenic areas in the Great Lakes region – even as overall Midwest population growth remains modest.

Interest Rate and Capital Market Trends: Macro forecasts suggest that the era of ultra-cheap capital is over, but we may see a gradual easing from the 2023–2025 highs. Fannie Mae’s latest economic outlook (as of Aug 2025) projects the 30-year mortgage rate will hover around 6.7% through late 2025, then ease to ~6.1% by end-2026. In other words, rates remain elevated relative to the 2010s, though potentially off their peak. This has a few implications for alternative assets: (1) Higher financing costs will keep homeownership out of reach for many – reinforcing the rental trend noted above. (2) Cap rates for niche assets may inch up, but if interest rates gently decline over the five-year horizon, we could see cap rate compression again in sectors with strong income growth (manufactured and senior housing are prime candidates, as investors chase their stable cash flows). (3) Debt availability might improve as lenders get more comfortable – already by late 2025 there are signs of credit markets stabilizing. The Federal Reserve began cutting rates in late 2025 (a total of 50 bps by November) amid cooling inflation. If this trend continues, the overall economy could avoid a hard landing, and liquidity in real estate lending should increase. We expect transaction volumes for alternative assets to pick up as well, albeit gradually – e.g., student housing investment rebounded in 2024–2025 after a 2023 lull, and this momentum may continue if institutional investors view these assets as defensive plays with attractive yields.

Demographics – Silver Tsunami and Gen Z Renters: The aging of the Baby Boomers will significantly bolster senior housing demand through the next five years and beyond. The 80+ population (primary clientele for assisted living and nursing care) will expand more rapidly in the late 2020s. NIC data already shows a strong post-pandemic recovery in senior housing: occupancy is rising and annual rent growth has stabilized around 4%. In fact, senior housing is projected to be one of the most profitable real estate asset classes going forward, as per NIC’s outlook. We anticipate occupancy in quality senior communities could push back above 90% by 2026–2027, effectively matching traditional multifamily levels for the first time in over a decade. This will, however, vary by region – some Midwest rural areas with out-migration might struggle to fill senior facilities unless they draw residents from wider catchment areas. Conversely, states like the Carolinas, Georgia, and parts of the Midwest with popular retirement locales (e.g., areas of Michigan or Wisconsin lake country) could see accelerated senior housing development. The challenge will be staffing and operational costs, but from a pure demand perspective, the arrow points up.

On the younger end, Gen Z and younger millennials will sustain student housing and entry-level rental demand. Despite a national dip in college enrollment earlier in the decade, total enrollment is expected to stabilize or grow slightly as the job market tightens and the “value of education” narrative holds. Cushman & Wakefield reports that as of the 2025/26 academic year, student housing fundamentals are robust – rents were up ~3.4% year-on-year and average occupancy ~91.6%, even with a decline in international students. Over the next five years, only a limited new supply of student housing is slated (2025 deliveries were down 42% from prior years), which should keep occupancies high. Importantly, investors are focusing on “core” student housing (modern, near-campus properties at large universities), especially in the Sunbelt and Southeast where enrollment growth is strongest. This could leave smaller or rural colleges with older housing stock at a relative disadvantage – a bifurcation where top schools’ housing is a hot asset, but second-tier markets see flat demand. Lenders will need to distinguish between those dynamics. Overall, barring an economic shock, we foresee steady performance in student housing, with national occupancies in the mid-90% range each fall and modest rent growth (~2–4% annually) as new supply remains constrained. Any significant change in federal education policy or student loan availability would be a wildcard to watch.

Midwestern Rural Trends: The Midwest hasn’t enjoyed the same spotlight as the Sunbelt, but it may quietly improve. During 2020–2022, remote work enabled some migration to rural areas across the country, though much of that was concentrated in resort/rural areas with amenities (e.g., parts of Mountain West). In the Midwest, we did see some “donut effect” benefits – smaller metro and non-metro areas drew interest as people left larger cities. Fannie Mae’s analysis found even non-metropolitan rural areas got an outsized demand boost in the pandemic era. Going forward, Midwestern rural markets could stabilize their long-running population declines. Affordable housing plus job opportunities will be key: for instance, if manufacturing re-shoring leads to new factories or distribution centers in Midwest heartland towns, expect a positive ripple in local real estate. We also note that the Midwest offers something increasingly valuable: climate resilience. Unlike the drought-prone West or hurricane-exposed South, many Midwest areas have abundant water and are less prone to extreme weather. This could, over a five-year horizon and beyond, start to factor into investor thinking (early evidence: some institutional investors have started looking at Midwest farmland and housing as a climate shelter). For underwriting rural assets, this means we might assign slightly less downside risk to well-located Midwest properties than we did a decade ago, when the narrative was purely out-migration. The macro implication is a potential narrowing of cap rate spreads between Midwestern secondary/rural and the national average, if these areas prove to have stable occupancy and rent trends.

Macro Cycles and Downside Considerations: Of course, no outlook is complete without considering risks. If the U.S. hits a recession in the next couple of years (a possibility if the Fed overshoots or external shocks occur), alternative assets would face real tests. Manufactured housing, often touted as recession-resilient, would likely hold up well – historically MHCs maintain high occupancy even in downturns because they are the housing of last resort for many (during the 2008–09 recession, lot occupancies dipped only marginally and quickly rebounded). In fact, current REIT reports show MHCs achieving double-digit NOI growth in 2025 with rent increases around 5%, indicating pricing power even in a high-inflation environment. Senior housing could face a tougher short-term challenge if a recession impacts seniors’ ability to sell their homes (often needed to finance entry into senior living) – but any dip is likely to be temporary given the demographic wave. Student housing tends to be less correlated with economic cycles (enrollments often rise in recessions), but smaller or private colleges could suffer from financial strain. Lenders should thus still stress-test these assets: e.g., what if occupancy drops 10% or expenses jump due to labor costs? Building the better data infrastructure as discussed will greatly aid in these stress-tests by providing empirical ranges from prior down cycles.

On the macroeconomic upside, if inflation remains in check and the Fed gradually lowers rates through 2026–2027 (as futures markets expect), we could see a sweet spot where alternative assets attract more capital. Investors, hunting for yield and stability, have already been rotating into sectors like MHCs, self-storage, and niche residential as defensive plays. The Clarion Partners analysis underscores that thematic tailwinds (aging population, housing shortage) are driving institutional interest in these “alternative” sectors. Over five years, we wouldn’t be surprised if these formerly alternative assets become much closer to mainstream in portfolios. As that happens, demand for better data will become a self-fulfilling prophecy – more investors will insist on transparency, and more owners will provide it.

Macro Summary: In the next five years, alternative real estate assets are poised for generally positive performance, underpinned by macro trends of housing unaffordability (boosting rental demand), demographic shifts (graying America and geographically dispersed workforce), and investors’ growing acceptance of these assets. Midwestern rural areas, while not booming, may see a modest revival or at least stabilization, which is a big improvement from past decades of decline. Lenders and investors who equip themselves with improved data and analytics on these segments will be best positioned to capitalize on the opportunities – and to navigate the risks – of this evolving landscape.

5. Leveraging Data Partners and Industry Resources (Backlink Hooks)

Improving data quality and underwriting for these alternative assets will not happen in a vacuum. Lenders and investors are encouraged to leverage external data vendors, rating agency research, and real estate analytics blogs that specialize in emerging property classes:

• Trepp – A leading provider of CRE and CMBS data. Trepp’s platforms contain loan performance details for multifamily, student housing, MHCs, etc., drawn from securitized deals. They offer analytical insights via TreppTalk blog and podcasts that frequently discuss niche sectors. For example, Trepp’s research on student housing highlighted the sector’s higher-than-average delinquency and the need for closer surveillance. Lenders can use Trepp’s data feed to benchmark a prospective loan’s underwritten NOI or DSCR against comparable loans in the database, helping quantify risk. Trepp also provides TreppAnalytics and TreppCRE tools that allow slicing data by property type – useful for tracking, say, how alternative property loans have been performing over time (delinquency trends, loss severity, etc.). Tapping into such resources can augment a lender’s internal data and reveal early warning signs (or positive trends) in these markets.

• Reonomy (Altus Group) – Reonomy aggregates vast amounts of property data, especially for small and mid-sized assets across the U.S. It is particularly useful for off-the-radar properties in tertiary markets – exactly the kind of assets we classify as alternative. Through Reonomy’s platform, one can research ownership records, property characteristics, tax assessments, and even some transactional data for rural properties, mobile home parks, and more. They also publish guides and articles (e.g., how to find off-market mobile home parks) that underscore best practices. Notably, Reonomy pointed out the high demand and high cap rates of mobile home parks, and also the fragmented ownership (only ~20% professionally owned) which means data is hard to gather. By using Reonomy’s property search and filters (for instance, filtering for properties tagged as “Mobile Home Park” nationwide), investors can build their own comp sets and start to fill data gaps. Reonomy essentially serves as a property intelligence tool that can uncover hidden comparables – a valuable service when traditional comp data isn’t published.

• CoreLogic – A powerhouse in real estate data, CoreLogic offers data on everything from home prices to rental rates and property risk factors. While much of CoreLogic’s publicized indices focus on single-family and conventional markets, they have data products (often under the Rental Trends or Investment Property suites) that can be relevant. For instance, CoreLogic’s Single-Family Rent Index gives insight into rental growth in various tiers of the market, which can serve as a proxy for rental trends even in smaller locales. CoreLogic’s databases can provide historical property valuations, which might help in rural appraisals (looking at past sales of even non-traditional properties like farm houses, etc., to identify price trajectories). Moreover, CoreLogic’s analytical services (RiskModel, climate risk scores, etc.) could be layered onto alternative assets – e.g., understanding tornado or flood risk for a manufactured home community in the Midwest. Engaging CoreLogic or similar data providers ensures that underwriters have access to the most up-to-date public records, MLS data, and even credit data to inform their analysis.

• Real Capital Analytics (RCA) – Now part of MSCI, RCA is renowned for tracking commercial property transactions globally. They historically focused on larger institutional deals, but they have expanded coverage to smaller deals via partnerships. RCA’s data on pricing trends and cap rates by sector is extremely useful. For example, RCA’s tracking shows that student housing trades picked up in 2024–2025, and that a significant share of volume has concentrated in certain high-growth states. RCA publishes trend reports (e.g., Real Estate in Focus: 2025 Trends to Watch) that often highlight alternative sectors’ performance relative to traditional ones. An investor can glean from RCA whether cap rate spreads between, say, manufactured home communities and apartments are widening or narrowing, which informs investment strategy. Additionally, MSCI’s Private Real Assets index expansions now include alternative property benchmarks, which are derived from RCA data contributions. Subscribing to these can give a forward-looking view on how capital is flowing – if we see, for instance, a compression in student housing cap rates in the Midwest relative to the Sunbelt, that signals increasing investor comfort which could trickle to lending terms.

• Rating Agency & Analytics Research (Moody’s, S&P, Fitch, Moody’s Analytics, etc.) – The credit rating agencies regularly publish sector commentaries that, while aimed at bond investors, contain useful data nuggets for lenders. Moody’s Analytics (the economic data arm) provides granular forecasts for home prices, rents, and even alternative asset performance scenarios. In one update, Moody’s highlighted how population shifts (more people moving South and West) are shaping housing demand and noted that the Midwest and Northeast’s slower growth could temper rent increases there. Such regional insights help lenders set realistic underwriting assumptions (e.g., don’t count on 5% annual rent growth in a rural Michigan student housing market if the local population is stagnant). Fitch Ratings and S&P have also commented on specialized REITs – for instance, Fitch might report on the portfolio metrics of a manufactured housing REIT or the performance of senior housing loans in HUD-guaranteed portfolios. These reports can validate or challenge underwriting assumptions. For example, if a rating agency reports that senior housing properties typically breakeven at 85% occupancy, a lender will know to scrutinize any borrower pro forma assuming 95% occupancy immediately.

• Industry Blogs and Publications – In addition to formal data sources, keeping a pulse on industry discussions via blogs is beneficial. Publications like Multi-Housing News, Senior Housing News, Student Housing Business, and ULI’s Urban Land magazine often feature research-driven articles. They frequently cite data from the likes of NIC, RealPage, Yardi Matrix, NAHB, and universities. For instance, Multi-Housing News recently noted that senior housing occupancy has improved for 17 straight quarters and is up 230 bps year-over-year. They also discuss challenges like labor shortages and construction slowdowns in that sector. Reading these pieces can alert lenders to on-the-ground issues that raw data might not immediately reveal (e.g., if many new rural rental projects are stalled due to construction costs, that implies less supply and potentially stronger rents ahead for existing stock).

In practice, a lender or investor should adopt a “mosaic approach” – combining internal loan performance data (or the Loan Analytics database figures) with external data feeds and expert commentary. By engaging with these data vendors and resources, one can fill the gaps identified in Section 1. Over the next five years, we expect data partnerships to become routine: banks might tie up with firms like Trepp or CoreLogic to integrate alternative asset data into their underwriting models, and investors might demand that sponsors provide third-party data benchmarks as part of due diligence.

Backlink Hooks Summary: The ecosystem of data and analytics available today can significantly improve underwriting outcomes for alternative real estate assets. Trepp’s loan-level insights, Reonomy’s property intelligence, CoreLogic’s broad datasets, RCA’s market transactions, and thought leadership from rating agencies and industry blogs collectively form the toolkit for a modern, well-informed underwriting process. Lenders and investors who proactively leverage these tools – rather than lamenting the lack of data – will gain a competitive edge in pricing and structuring deals in the rural, manufactured, senior, and student housing arenas. In turn, as more players adopt data-driven approaches, the increased transparency and performance track record will likely attract even more capital to these sectors, creating a virtuous cycle of better data and better financing options.

Sources:

• Loan Analytics Database (Property Management in the US, 2025) – internal industry data

• Loan Analytics Database (Real Estate and Rental and Leasing in the US, 2025) – internal industry data and forecasts

• Fannie Mae Economic & Housing Insights – Rural Housing Demand Since the Pandemic (Nov 2024)

• Trepp Research – Student Housing Loans Cheat Sheet (2019)

• Reonomy/Altus Group – Mobile Home Parks Investment Guide (2023)

• NIC MAP Vision – Senior Housing Occupancy 2025 (NIC Analytics webinar)

• Cushman & Wakefield – Student Housing Trends 2025

• Clarion Partners – U.S. Real Estate Cycle Emerging (2025 Outlook)

• Multi-Housing News – Moody’s Update: Population Shifts (Jan 2025)

• Newmark & MSCI Real Capital Analytics – Student Housing Transactions Press Release (Oct 2025)

• RealPage/MSCI – Apartment Transactions 2Q 2025

• Moody’s/NAMU – Housing Market Outlook 2025–2026