Nature of Data Provided
The data collected online and offline is provided after being processed on a uniform standard through an algorithm.
Comprehensive visibility over the lodging markets of Korea
With blurring boundaries among lodging property types, the competitive environment in the lodging industry is getting fiercer. In other words, it is becoming increasingly important to precisely evaluate the competitive environment to optimize the positioning strategy. However, market information asymmetry among lodging property types is a hurdle.
Comprehensive visibility into the market is the starting point for the structured value chain to work. The structured value chain allocates separate responsibilities and obligations to operators, owners, investors, and advisors, at different capital costs and time horizons. In other words, each deals with risks of different nature, which need to be managed in an integrated manner for the value chain to function properly.
Lobin has financial data for 81% of all lodging properties across the country. Local government statistics provide all supply information, but without financial data, and the Korea Hotel Association statistics provide supply and revenue data, but only for hotels. Lobin provides facility mix, operating performance, financial position, and asset value data for all types of properties witha footprint of operations.
Data processed based upon industry expertise and technology
There are various public statistics and private data related to the lodging industry in Korea, whether domestic or international. Lobin collects and utilizes most of them, but does not directly provide them. There are two main reasons for this.
- First, discrepancies between statistical data are widespread. Public statistics for the lodging industry in Korea are compiled according to the statutory classification system for lodging businesses and properties. However, the statistics are compiled independently as relevant authorities separated by type. Also, there is a big difference in the way and the scope each statistics are aggregated because the focus of each authority is not the same.
- Second, contamination of survey-based statistics is inevitable. There are contaminations in supply statistics due to a mix-up between block and street address systems, and also in the operating performance statistics due to incorrect survey responses without practical way to correct them. Although various techniques to validate and correct statistical significance are widely used, there are limitations to unconventional lodging industry data.
Robin provides data processed through artificial intelligence algorithms combining expertise and technology. We process various statistical data through matching, correcting, estimating, and forecasting through our own machine learning algorithm based on the expertise in the lodging industry. Lobin's algorithm captures the volatility of the lodging market arising out of asymmetry between elastic demand and inelastic supply, more accurately than existing algorithms.
Clear criteria for handling data to prevent misuse of data
Lobins utilizes individual property level data based on its own classification system for property types. Individual property level data are processed according to Lobin Property Types, which breaks down the statutory property types according to market conditions. It is intended to maximize the usability of available statistical data while reflecting the market practice more accurately. Please refer to the Lobin Property Types ↗ page in this regard.
Users can directly cluster individual property level data to extract competitive market level data. This enables owners and operators of individual properties to quickly respond to market volatility by creating a competitive market with properties selected directly, extracting the combined financial data and comparing it to the subject property's financial data.
The financial data provided by Lobin is vested in the property, not in the ownership. This is because the focus is to measure the impact of supply and demand dynamics in the market and the positioning of an individual property, by excluding the impact of the owner's non-operating financial position. Therefore, non-cash income or expense data are not provided, and net operating income (NOI) rather than net income (NI) is used as the operating profit indicator.
- USALI: In principle, data related to operating performance, such as revenues, expenses, and profits, are subject to the USALI. USALI is considered more appropriate than IFRS to assess productivity and efficiency of lodging properties and identify opportunities for improvement.
- IFRS: In principle, data related to financial position, such as assets, liabilities, and equity, are subject to the IFRS. However, if the actual data collected and utilized by Lobin is prepared in accordance with GAAP, it is not converted to IFRS standards, so user caution is required.
Contact for Data Services: dboard@lobin.co
