Financial Modeling Prep (FMP) Developer API - Office REIT Financials

It is the statement-grade fundamental layer for the listed office sector: a single platform of roughly 270 documented endpoints spanning real-time market data, historical prices, financial statements, index data, economics, company profile, analyst estimates, earnings transcripts, 13F positions, insider trading, ESG scores, ETF and mutual-fund holdings, commodities and forex. Datadory packages the equity-fundamental families into one rectangular extract per office REIT ticker.

The relevant families for office-REIT work are the three statements - income-statement, balance-sheet-statement and cash-flow-statement, each with as-reported, trailing-twelve-month and growth variants; key-metrics and ratios with TTM versions; discounted-cash-flow and custom-discounted-cash-flow valuations; dividends plus a dividends calendar; end-of-day price history in light and full depths alongside unadjusted series; and a company screener that filters by market cap, price, volume, beta, sector and country. Bulk endpoints paginate the whole listed universe - profiles, balance sheets, cash flows, DCF values and ratings by year and period.

There is no REIT-specific endpoint family. Boston Properties (BXP), Vornado (VNO) and Kilroy Realty (KRC) are retrieved through exactly the same statement, ratio and price endpoints as any industrial - which is precisely the point: one schema across every name you screen means an office-REIT comp sheet is a filter, not an integration project. Sector and industry fields on the company profile object isolate real-estate names programmatically. Get a sample of this dataset scoped to your tickers.

Three shapes arrive together - the profile that fixes who the row is about, the period-stamped statement rows, and the valuation and distribution records that hang off them. The field names below are documented in the platform's reference; value slots fill in once we cut your sample:

# company profile -- one flat object per ticker (identity + classification)
symbol          : BXP
companyName     : BXP, Inc.               exchangeName : New York Stock Exchange
sector          : Real Estate             industry     : Office REITs
isReit          : true                    ipoDate      : <first listing date>

# income statement -- one record per symbol per period (quarterly or annual)
date            : <fiscal period end>     calendarYear : <year>      period : Q1-Q4 / FY
revenue         : <standardized revenue>  grossProfit  : <gross profit>
netIncome       : <standardized net income>

# balance sheet -- one record per symbol per period
totalAssets     : <total assets>          totalLiabilities : <total liabilities>
totalEquity     : <total stockholders equity>

# cash flow -- the lines REIT analysts actually read
operatingCashFlow : <cash from operations>   freeCashFlow : <free cash flow>

# ratios and key metrics -- one record per symbol per period, TTM available
peRatio         : <price to earnings>     pbRatio      : <price to book>
dividendYield   : <dividend yield>

# discounted cash flow -- one model-implied value per ticker
dcf             : <model-implied intrinsic value>

# ... repeats per ticker across the US-listed office universe (BXP, VNO, KRC, SLG, ARE...)
#     bulk variants pull the full listed universe by year and period in paginated parts

Read the profile and you have the entity, its exchange and its classification in five fields. Read the statement rows and you have every period stamped with fiscal year and quarter, so a ten-year quarterly history assembles with a sort instead of per-filing assembly. Note what the delivery layer has already done: the standardized statements are line-item aligned across companies, so revenue for BXP sits in the same column position as revenue for KRC - which is what makes cross-ticker comp tables possible without mapping each filing by hand.

Eight field groups form the documented core, each verified against the platform's reference during the August 21, 2026 research pass. The symbol string is the primary query parameter everywhere - one ticker joins profile, statements, ratios, dividends and price series into a single entity view. Period identifiers (date, calendarYear, period) stamp every fundamental record so quarterly and annual cuts never blur together.

The statement groups carry the analytical weight for this sector. operatingCashFlow and freeCashFlow matter more than net income for office REITs, because GAAP earnings absorb decades of depreciation on buildings that are not actually wasting away; the funds from operations definition explains the adjustment convention. FFO itself is not a native field here - it derives cleanly from the cash-flow and depreciation lines the statements do ship, and teams that want it pre-computed pair this record with sources that publish it natively.

dcf deserves a second look: it is a model-implied intrinsic value computed server-side, not a market price. Set it against the quote-page price for the same ticker and you have a ready-made over/under-valued spread without building a discounting model yourself. Everything beyond these groups - analyst estimates, transcripts, insider transactions, ESG scores, ETF holdings - folds under additional fields on request rather than bloating every row.

Geography - global listings coverage, deepest on US exchanges. Every US-listed office and diversified REIT is in scope: BXP, VNO and KRC anchor the verified set, and the same endpoints serve the rest of the roster without exception. The company screener's sector and industry fields let you enumerate the real-estate slice programmatically instead of maintaining a hand-built ticker list.

Temporal - two clocks run at once. Fundamentals move at reported-quarter cadence: standardized statements arrive quarterly and annually with multi-year history behind them, audited and standardized. Prices run faster - intraday intervals from one minute up to daily, with adjusted and unadjusted end-of-day series for backtests that need to separate splits from performance.

Granularity - one record per symbol per period for fundamentals; one record per symbol per interval for prices. The bulk variants invert the axis: a single call returns every covered symbol for one year-and-period combination, paginated in parts, which is how whole-university screens get loaded overnight instead of ticker-by-ticker.

Set against the wider catalog - average quality score 7.81 across the datasets we package - this record scores 7/10: fully verified endpoint families and field semantics, with response payloads confirmed against the documentation rather than observed live.

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

Hourly suits event windows around prints and dividend declarations, when the gap between a model-implied value and the tape reprices fastest. Daily is the fundamentals standard: one capture per session keeps ratios current with the close while accumulating a clean time series. Weekly suits board packs and comp-sheet refreshes where the quarter, not the session, is the unit of analysis. Whichever channel and cadence you pick, the field dictionary above travels unchanged - moving from daily quotes to quarterly statement pulls is a cadence change, not a migration.

• Backtesting office-REIT strategies - multi-year statement, ratio and price history loads whole-universe panels; the bulk variants make the load a pagination loop rather than a ticker crawl.
• Comp-sheet construction - standardized line items align revenue and equity across BXP, VNO and KRC, so relative multiples compute in one join instead of per-filing mapping.
• Valuation screens - set the model-implied dcf beside the live price for every name in the sector and rank the spread; the custom-discounted-cash-flow variant accepts your own assumptions.
• Dividend-policy tracking - the dividends family carries per-distribution records plus a forward calendar, feeding payout-ratio and coverage monitors that survive schedule changes.
• Universe maintenance - the company screener filters by market cap, price, volume, beta, sector and country, keeping an office-REIT constituent list generated rather than hand-curated.
• Cross-source reconciliation - statement-level depth extends quote-page ratio snapshots into full financials; the field-by-field split between the two layers lives in the Yahoo Finance office REIT record.

Investors and quants get the heaviest lift: statements deep enough to build factor libraries on, priced at zero schema friction - office REITs for investors and quants. Data scientists and engineers get typed rectangular rows that land analysis-ready in a warehouse, plus bulk endpoints that keep whole-market refreshes inside a nightly window - office REITs for data scientists and office REITs for developers and builders. Market researchers and consultants get citable per-company fundamentals behind sector claims without touching a filing PDF - office REITs for market researchers. Industry context - where this record sits among Nareit's T-Tracker aggregates, NCREIF composites and building-stock microdata - lives on the office REITs data hub, and the ranked shortlist on best office REITs datasets shows where it lands against alternatives.

Three things worth knowing upfront.

First, the FFO boundary: funds from operations is not a native column here. It derives cleanly from operating cash flow, depreciation and net income - all shipped - but if you want FFO delivered as a finished number, say so when you request the sample and we scope the derivation into the extract rather than leaving it as pipeline homework.

Second, the standardization trade: statements are standardized, not as-reported facsimiles. Line-item alignment across companies is the feature that makes comp sheets possible; if your models need the raw filing presentation, the as-reported variants cover that too - tell us which cut matters and the sample ships both side by side.

Third, the snapshot design: each capture is point-in-time. A scheduled repeat turns the snapshot into the panel this record is built for - ticker x period x field, growing backward-compatible forever. Name the tickers, the periods and the cadence you care about and the sample comes back shaped to them.