Motivation
Before numbeo.com there were several other reports about cost of living indexes, i.e. reports from Mercer, UBS and Economist. However, in these reports usually data behing research was hidden or expensive to purchase. There were no guarantees that their data are correct. Manual collection of cost of living data is error prone since someone can write down wrong number or just wrote down the price of expensive milk because he didn't see other milk packages in the supermarket at the other shelf. Their reasearch was very limited in number of cities included in reasearch and couldn't scale without big increase in expenses. There were no insight about error rate in their manually collected data. They publishes just a index, which is not enough for a personal estimation since a person is not an average person due to different lifestyles like :- size of a family (number of dependent persons)
- dining out or eating at home
- drinking alcocholic drinks and smoking or not
- gives entire dataset for free
- allows a person to estimate they own expenses
- uses visdom of the crowd to get as reliable data as possible
- provides a system for systematic reaserch of cost of living and property markets
- provides a system for other systematic economical researches on huge dataset with worldwide data
Methodology
Collecting and processing data
To collect data Numbeo rely on user inputs and manually collected data from authority sources (websites of supermarkets, governmental institutions, other reaserches, etc.). There are automatic and semi-automatic filters to filter out noise data. Filter works as follows : if, for a particular price in a city, in short time span, values are 5, 6, 20 and 4, the value 20 is discarded as noise and the average of remaining data can be used to estimate that the average price of that item in the city is 5. Or shortly speaking Numbeo uses heuristic techonology. Using existing manually reviewed data Numbeo periodically discards data which most likely are incorrent statistically. Due to higher number of inputs for a country than for a city, data showed on a country level contains lower noise than data showed on a city level. Filters works better when there are more inputs.Calculating indexes
Numbeo indexes is a best guess of relative average expenses in a given city. Weights are subject of change over time. But since methodology is not hidden, as the moment of writing this weights are as this :
mysql> select name, category, cpi_factor, rent_factor from item;
+------------------------------------------------------------------+------------------------+------------+-------------+
| name | category | cpi_factor | rent_factor |
+------------------------------------------------------------------+------------------------+------------+-------------+
| Meal, Inexpensive Restaurant | Restaurants | 10 | 0 |
| Meal for 2, Mid-range Restaurant | Restaurants | 2 | 0 |
| Combo Meal at McDonalds or Similar | Restaurants | 10 | 0 |
| Domestic Beer (0.5 liter draught) | Restaurants | 12 | 0 |
| Imported Beer (0.33 liter bottle) | Restaurants | 12 | 0 |
| Coke/Pepsi (0.33 liter bottle) | Restaurants | 12 | 0 |
| Water (0.33 liter bottle) | Restaurants | 12 | 0 |
| Milk (regular, 1 liter) | Markets | 30 | 0 |
| Loaf of Fresh Bread | Markets | 30 | 0 |
| Eggs (12) | Markets | 6 | 0 |
| Cheese (1kg) | Markets | 4.5 | 0 |
| Water (1.5 liter bottle) | Markets | 20 | 0 |
| Bottle of Wine (Mid-Range) | Markets | 2 | 0 |
| Domestic Beer (0.5 liter bottle) | Markets | 5 | 0 |
| Imported Beer (0.33 liter bottle) | Markets | 5 | 0 |
| Pack of Cigarettes (Marlboro) | Markets | 12 | 0 |
| One-way Ticket | Transportation | 20 | 0 |
| Monthly Pass | Transportation | 1.5 | 0 |
| Taxi (within center, approx. 5km) | Transportation | 10 | 0 |
| Apartment (1 bedroom) in City Centre | Rent Per Month | 0 | 0.25 |
| Apartment (1 bedroom) Outside of Centre | Rent Per Month | 0 | 0.25 |
| Apartment (2 bedrooms) in City Centre | Rent Per Month | 0 | 0.25 |
| Apartment (2 bedrooms) Outside of Centre | Rent Per Month | 0 | 0.25 |
| Basic (Electricity, Gas, Water, Garbage) | Utilities (Monthly) | 1 | 0 |
| Mobile Phone 100 Minutes Call | Utilities (Monthly) | 15 | 0 |
| Internet (1 Mbps ADSL flat) | Utilities (Monthly) | 1 | 0 |
| Price per Square Meter to Buy Apartment in City Centre | Buy Apartment Price | 0 | 0 |
| Price per Square Meter to Buy Apartment Outside of Centre | Buy Apartment Price | 0 | 0 |
| Median Monthly Disposable Salary (After Tax) | Salaries And Financing | 0 | 0 |
| Mortgage Interest Rate in Percentanges (%), Yearly For USD / EUR | Salaries And Financing | 0 | 0 |
| Gas (1 liter) | Transportation | 80 | 0 |
+------------------------------------------------------------------+------------------------+------------+-------------+
When calculating Local Purchasing Power, formula which is used for weights is 1.5 * cpi_factor + rent_factor.
This correction factor is used to estimate other goods and services which is not tracked at this website.
Local_Puchasing_Power_Index = Affordability(This_City) / Affordability(New York)
Affordability (of a city) = Average_monthly_disposable_income / sum_of (Price_in_the_city * (1.5 * cpi_factor) + rent_factor)
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