-
1 Comment
Daibiru Corporation is currently in a long term uptrend where the price is trading 11.3% above its 200 day moving average.
From a valuation standpoint, the stock is 21.4% cheaper than other stocks from the Real Estate sector with a price to sales ratio of 3.8.
Daibiru Corporation's total revenue rose by 2.9% to $11B since the same quarter in the previous year.
Its net income has dropped by 3.9% to $2B since the same quarter in the previous year.
Based on the above factors, Daibiru Corporation gets an overall score of 3/5.
| ISIN | JP3497200000 |
|---|---|
| Sector | Real Estate |
| Industry | Real Estate Services |
| CurrencyCode | JPY |
| Exchange | TSE |
| PE Ratio | 32.25 |
|---|---|
| Target Price | 1425 |
| Dividend Yield | 0.8% |
| Market Cap | 252B |
| Beta | 1.29 |
Daibiru Corporation, together with its subsidiaries, owns, operates, manages, and leases real estate properties in Japan. It owns and leases 29 properties, including offices, hotel buildings, and retail complexes in central Tokyo, Osaka, and Sapporo. The company also develops and leases residence halls for students; and apartment buildings for business people on temporary assignment, as well as condominiums for families. In addition, it offers facilities, cleaning, and security management services; and real estate agency services. The company was formerly known as Osaka Tatemono Co., Ltd. and changed its name to Daibiru Corporation in January 1992. The company was incorporated in 1923 and is headquartered in Osaka, Japan. Daibiru Corporation is a subsidiary of Mitsui O.S.K. Lines, Ltd.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 8806.TSE 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 2025
