Nareit Mortgage REIT Sector Overview - Live Metrics & Constituent List

It is the mortgage industry's own count of itself - the page Nareit, the national association for the sector, maintains to define and measure the mREIT cohort. The definition comes first: mortgage REITs finance income-producing real estate by purchasing or originating mortgages and mortgage-backed securities, earning income from the interest on those holdings rather than from property rent. Then the measurement, in two layers.

The upper layer is the sector aggregates, sourced to FTSE Russell: 30 tracked vehicles, a 13.14% dividend yield, a +0.76% year-to-date total return, -1.55% for July 2026 alone and +16.02% for calendar 2025, all dated month-end 7/31/2026. Beside them sits a quarterly T-Tracker block logging $2,369 million of dividends paid in Q1 2026 and $8,628 million across full-year 2026 - though the other T-Tracker panels render empty on this view, so treat it as a payout record rather than a fundamentals panel.

The lower layer is what makes this record unique in Datadory's catalog of 1,744 datasets: a sortable roster of the actual listed companies, roughly 22 names per pull, each carrying ticker, headquarters location, trailing one-year total return and last close stamped to the 08/21/2026 market close. Within the catalog this record scores 8/10 against a 7.81 mean. Get a sample of this dataset scoped to your names before you build on it.

One row per listed company, keyed on ticker. Five illustrative rows in exactly the delivered shape:

company_name            : ACRES Commercial Realty Corp.
ticker                  : ACR
sector                  : Mortgage
one_year_total_return_pct : -27.98
price_usd               : 14.62

company_name            : AGNC Investment Corp.
ticker                  : AGNC
sector                  : Mortgage
one_year_total_return_pct : +28.88
price_usd               : 10.92

company_name            : Angel Oak Mortgage, Inc.
ticker                  : AOMR
sector                  : Mortgage
one_year_total_return_pct : +0.82
price_usd               : 8.72

company_name            : Apollo Commercial RE Finance, Inc.
ticker                  : ARI
sector                  : Mortgage
one_year_total_return_pct : +11.56
price_usd               : 6.94

company_name            : Arbor Realty Trust, Inc.
ticker                  : ABR
sector                  : Mortgage
one_year_total_return_pct : -48.99
price_usd               : 5.18

Read together they are a portrait of the sector's dispersion in five lines. AGNC Investment earns +28.88 percent over twelve months while Arbor Realty Trust loses 48.99 percent - a seventy-seven-point spread inside one asset class, which is precisely what cap-weighted instruments average away and this roster refuses to. Angel Oak Mortgage sits nearly flat at +0.82 percent, the middle case between the two tails. Prices run from $5.18 to $14.62 across the same five names, all stamped to one market close so no vintage mixing creeps in. Every row reads Mortgage in the sector column because this page is the mortgage slice of the REIT universe, enumerated. Live rows cut to whichever tickers you name - or the full roster - arrive in this identical shape; get a sample and join on ticker.

Six fields anchor every delivered row, verified during the August 2026 research pass. They divide three ways.

Identity - company_name and ticker. The name is the roster's primary key as published; the ticker is the join key everything downstream keys on, and it stays blank where a vehicle carries no listed symbol, which matters more than it sounds because the blanks mark the boundary of the investable set.

Placement - location, the headquarters city and country, and sector, the classification enum reading Mortgage on every row of this particular roster.

Economics - one_year_total_return_pct, the trailing twelve-month total return as of the stated market close, and price_usd, the last closing share price on that same close. Both columns print Not available for a handful of entries - InPoint Commercial, PennyMac and Rithm among them in the captured pull - and the honest handling of those gaps is documented rather than smoothed over, because a stale figure silently joined into a model costs more than a missing one.

Beyond the verified core, the sector aggregates, the quarterly dividends-paid ledger and directory-page enrichment sit under additional fields on request: named at sampling, delivered as typed columns.

Geography - United States throughout. The roster counts the country's listed mortgage REIT cohort outright, and each row carries its own headquarters city and country, Bethesda United States for AGNC being the pattern. No global extension exists at this altitude; international property exposure arrives through the equity-side series elsewhere on the shelf.

Temporal - this record is deliberately present-tense, and its two clocks are worth keeping separate. The sector aggregates carry their own as-of date of month-end 7/31/2026; the per-company returns and prices stamp to the 08/21/2026 market close; the dividends-paid ledger reports quarterly, most recently $2,369 million for Q1 2026. There is no deep history here - the rolling current state only. For fifty-plus years of monthly mortgage REIT returns, the neighboring complete monthly index workbook owns that job.

Granularity - two altitudes in one record. Roughly thirty vehicles tracked at the sector level, about twenty-two listed rows per pull at the company level, one row per company with return and price attached. The gap between the two counts is itself information: tracked vehicles include entries beyond the currently listed-and-computable set, which is why row counts vary slightly pull to pull.

API, files, or your warehouse. Daily, weekly, or hourly.

Your cadence is decoupled from how the underlying page refreshes: most teams load the roster once, then keep a table current so each new pull appends cleanly onto the prior close - new rows appearing, prices stepping forward, the aggregates updating in place at month-end. Deliveries arrive normalised to the field dictionary above, keyed on ticker with blanks flagged rather than dropped, in JSON, CSV, spreadsheet-ready Excel tables or XML. The sector aggregates travel beside the roster rows with their own as-of dates intact, so nothing downstream mistakes a 7/31/2026 sector yield for an August company-level one.

• Watchlist and universe maintenance - the enumerated listed cohort, roughly twenty-two names with tickers and prices, replaces hand-built mREIT lists that go stale the week after they are compiled.
• Yield benchmarking - any holding's payout can be checked against the 13.14% aggregate sector yield dated 7/31/2026, the comparison a dividend-claim memo actually turns on.
• Dispersion and factor research - per-company one-year returns spanning +28.88% to -48.99% in a single window let spread, book-value sensitivity and idiosyncratic risk be studied at name level rather than through a basket's average.
• Income analysis - the quarterly dividends-paid ledger ($2,369 million in Q1 2026) gives coverage ratios and distribution forecasts a sector-wide denominator.
• Territory building and CRM loads - company names with headquarters cities attached make sales segmentation between agency home-financing and commercial-credit names a filter rather than a project.
• Market commentary and citation - association-published aggregates with explicit as-of dates give journalists, analysts and academics quotable numbers whose lineage survives scrutiny.

Investors and quant researchers (relevance 3/3) get the listed cohort at per-name resolution - dispersion, yield checks and the payout ledger in one pull; see investors quants use cases. Market researchers and consultants (2/3) get citable aggregates plus a roster to size the competitive set against; see market researchers use cases. Journalists, academics and students (2/3) get association-published figures with explicit dates, safe to cite; see journalists academics use cases. Sales and growth teams (2/3) get a territory-ready company list with locations attached; see sales teams use cases. Developers building data products get one small, stable six-field schema keyed on ticker that loads once and diffs forever.

Within mortgage REITs, this record owns the census layer: it is the only mREIT-native roster in the slice, counting the cohort company by company under official sector aggregates. Its nearest sibling works the other end. The iShares Mortgage Real Estate Capped ETF (REM) is the basket - twelve fields describing one tradable fund tracking the FTSE Nareit All Mortgage Capped Index since May 2007, with Annaly at 25.74% of assets and a 0.48% expense ratio. That trade-off is worked through row by row in the Nareit roster vs the REM basket.

Above both sits the benchmark system: Nareit REIT Indexes and Market Performance Landing and its complete monthly workbook supply the fifty-year monthly return history this record deliberately lacks, including the Home Financing versus Commercial Financing split. The Nareit Data & Research Hub bundles the portal views, and the Federal Reserve's H.15 daily yield curve frames the funding cost an mREIT's spread lives on. If the question starts with a company's name, take this roster; see the ranking on best mortgage REITs datasets, the pooled shelf on the mortgage REITs data hub, or the Nareit source profile.

Three things worth settling upfront. First, name your scope: the full roster runs to roughly two dozen rows and joins cleanly on ticker, but samples scoped to named tickers - or to the roster plus the aggregates side by side - beat the firehose. Second, respect the two clocks: aggregates dated 7/31/2026, prices stamped to the 08/21/2026 close, dividends quarterly, so any query joining company-level and sector-level figures should align on the as-of columns rather than assume one timestamp. Third, mind the gaps: several entries print Not available returns and PennyMac showed $0.00 in the captured pull, stale-versus-unlisted remains an open question the research pass flagged honestly, and the T-Tracker fundamentals panels render empty on this view - so treat the record as a census and a payout ledger, and pair it with loan-level or issuer-fundamentals records when those questions matter.