Data > Data Processing
Data Processing
In the lodging industry, the asymmetry of information deepens, bringing up the level of risks, while supply is continuously diversifying and demand is becoming more volatile. The best way to handle this growing level of uncertainties is to secure as many options as possible. Data is the most efficient tool to excavate options.
1. Value of Data
Lobin's comprehensive lodging database aims to help individual lodging establishments respond effectively to lodging market risks through data-driven decisions. In a dynamically changing lodging market, data holds the following significance:
- In the lodging market, the asymmetry between inelastic supply and elastic demand amplifies the volatility of cash flow in both short and long terms. To respond effectively to such volatility, comprehensive data that provides a full visibility into the market is essential.
- As lodging establishments diversify, competition is no longer limited to the type of lodging establishments. Increasingly, different types of establishments are competing for the same demand, making a comprehensive visibility into the entire market more critical.
2. Collecting Data
Lobin's comprehensive lodging database is based on actual data available. It is primarily composed of domestic and international statistical data that can be collected and utilized in relation to the lodging market of Korea. The database includes the following types of statistical data:
Category
Source
Supply
Property
Building Ledger, Building Permits Ledger, Property Records
Enterprise
Business Registration
Establishment
Lodging Business Ledger, Tourist Lodging Business Ledger, Rural Minbak Ledger, Urban Minbak Ledger
Demand
Domestic
Domestic Travel Survey (2005-), Hotel Operating Statistics (2005-)
International
International Travel Survey (2005-), Hotel Operating Statistics (2005-)
Financial
Revenue
Economic Cencus (MDIS, 2010, 2015), Service Industry Survey (MDIS, 2005), Hotel Operating Statistics (2005-)
Profitability
Economic Cencus (MDIS, 2010, 2015), Service Industry Survey (MDIS, 2005), Financial Statement Analysis (2005-)
Others
Service Industry Survey (MDIS, 2005), Financial Statement Analysis (2005-), Tourism Business Statistics (2005-2009)
3. Processing Data
However, due to the differences in scope and format among the various actual data collected, the data is processed according to consistent standards. The goal is to ensure that the data can be used for decision-making related to lodging business management and lodging property investment, based on the following principles:
- Lobin aims to achieve complete visibility of the entire lodging market. While various types of lodging establishments are competing, visibility into the competitive environment is limited due to imbalanced availability of data. We work to fill in the blind spots of the lodging market.
- Lobin aims to achieve visibility over long-term changes in the lodging market. The most reliable clue to addressing growing uncertainty is the pattern of past volatility. We work to reduce uncertainty by meticulously analyzing long-term time-series patterns.
- Lobin's data covers the entire value chain of the lodging industry. To address the volatility of cash flow in the lodging industry, a seamless financial management system is essential. We work to provide comprehensive data, including revenues, expenses, and financial status.
- Lobin's data is based on individual lodging establishments. Lodging market data can only be meaningful when it is actively utilized by individual establishments. We work to enhance data usability at the individual lodging establishment level.
4. Correcting and Estimating
In addition, Lobin corrects errors caused by discrepancies in items or figures among various actual data available, and estimates missing values. Particularly, we estimate missing values using an algorithm developed by ourselves to captures regional and type-specific cyclicality and seasonality.
Category
Details
Correction
Subject
Demand & financial data with different values for the same item
Method
1) Identify independent variables and derive relevant fonctions
2) Independent variable error: replace with the value in confirmed statistics
3) Relevant function error: correct through history & benchmarking analyses
2) Independent variable error: replace with the value in confirmed statistics
3) Relevant function error: correct through history & benchmarking analyses
Standard
Correct sellable unit values by establishment and reflect sales volume
Validation
Compare with the sum in the confirmed statistics (same sample, 95% confidence level)
Estimation
Subject
Detailed items of demand & financial data with values missed
Method
1) Identify independent variables for the data item
2) Derive functions for cyclicality and seasonality by region and type
3) Estimate missing value through history & benchmarking analyses
2) Derive functions for cyclicality and seasonality by region and type
3) Estimate missing value through history & benchmarking analyses
Standard
Correct sellable unit values by establishment and reflect sales volume
Validation
Compare with the sum in the confirmed statistics (same sample, 95% confidence level)
* The history analysis refers to a comparative analysis against previous indicators of the establishment itself, and the benchmarking analysis refers to a comparative analysis against recent indicators of competitive establishments.