-
1 Comment
FRP Holdings, Inc is currently in a long term uptrend where the price is trading 14.0% above its 200 day moving average.
From a valuation standpoint, the stock is 101.8% more expensive than other stocks from the Real Estate sector with a price to sales ratio of 20.0.
FRP Holdings, Inc's total revenue rose by 1.0% to $6M since the same quarter in the previous year.
Its net income has dropped by 84.0% to $393K since the same quarter in the previous year.
Finally, its free cash flow fell by 102.3% to $-724K since the same quarter in the previous year.
Based on the above factors, FRP Holdings, Inc gets an overall score of 2/5.
| Exchange | NASDAQ |
|---|---|
| CurrencyCode | USD |
| ISIN | US30292L1070 |
| Sector | Real Estate |
| Industry | Real Estate Services |
| Market Cap | 459M |
|---|---|
| PE Ratio | 95.96 |
| Target Price | 45 |
| Beta | 0.56 |
| Dividend Yield | None |
FRP Holdings, Inc. engages in the real estate business in the United States. It operates through four segments: Industrial and Commercial, Mining Royalty Lands, Development, and Multifamily. The Industrial and Commercial segment owns, leases, and manages commercial properties. The Mining Royalty Lands segment owns, leases, and manages mining royalties in Florida, Georgia, and Virginia. The Development segment owns and monitors the use of parcels of land that are in various stages of development; and acquires and constructs new apartment, retail, warehouse, and office buildings. The Multifamily segment owns, leases, and manages buildings through joint ventures. FRP Holdings, Inc. was incorporated in 2014 and is based in Jacksonville, Florida.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for FRPH using our backtest tool. PyInvesting provides the backtesting software for you to backtest your investment strategy. Our backtest software is written using Python code and allows you to backtest stock, backtest etf, backtest options, backtest crypto and backtest forex online. Our backtesting Python framework is highly robust and gives you a realistic simulation of how your strategy would have performed in the past using backtest data.
© PyInvesting 2026