Methodology and motivation for Numbeo.com
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 the data behind the research was usually hidden or expensive to purchase. Furthermore, there were no guarantee
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 on the other shelf. Also, currently milk can have discounted price at the moment,
or current shortages of milk make milk more expensive. Also, price of milk might be different depending of supermarket chains.
Their reasearch was very limited in terms of the number
of cities included in the reasearch and couldn't scale without big increase in expenses.
Also, there is no insight about the error rate in their manually collected data.
Some of those reports publishes just a index, which is not enough for
a personal estimate since a person is not an
average person due to different lifestyles such as:
- size of a family (number of dependent persons)
- dining out or eating at home
- renting or owning an apartment
- driving or using a public transport
- drinking alcocholic drinks and smoking or not
Other cost of living sources don't provide a systematic way to extract custom indexes. Numbeo.com provides a world-class software
for extracting various economic indicators for free.
Before the Great Recession (World Economic Crysis of 2007-2009) price of properties worldwide tended to look like a crazy to
the founder of this website. The price of small flat in a third world country he currently lives in was same as 310 ultra
modern TFT monitors at that time. The wild speculation in property prices suggested that people really needed a tool for a
speculation or to turn their speculation down.
So, that's how Numbeo was born. Numbeo :
- provides to a reader of a website prices for free
- allows a person to estimate they own expenses
- uses the wisdom 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 research on huge dataset with worldwide data
Methodology
Collecting and processing data
To collect data Numbeo relies on user inputs and manually collected data from authoritive sources
(websites of supermarkets, governmental institutions, other surveys, etc.). There are automatic and
semi-automatic filters to filter out noise data. The simplest filter is working as follows : if, for a particular price in a city,
values are 5, 6, 20 and 4 in short time span, the value 20 is discarded as noise and the average of the remaining
data can be used to estimate that the average price of that item in the city is 5. There are most sofiscated filters in use.
The filters are performing better when there are more inputs.
Or to put it briefly, Numbeo uses heuristic techonology.
Using the existing data Numbeo periodically discards data which most likely are incorrent statistically. Numbeo also
archives the values of old data (currently data which are more than 18 months old). The values of old data are preserved
tu be used for historical purposes.
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.
Calculating indexes
Numbeo indexes are 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 follows :
mysql> select name, category, cpi_factor, rent_factor from item;
+-------------------------------------------------------------+------------------------+------------+-------------+
| name | category | cpi_factor | rent_factor |
+-------------------------------------------------------------+------------------------+------------+-------------+
| Meal, Inexpensive Restaurant | Restaurants | 19.8 | 0 |
| Meal for 2, Mid-range Restaurant, Three-course | Restaurants | 6 | 0 |
| Combo Meal at McDonalds or Similar | Restaurants | 6.6 | 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 White Bread (500g) | Markets | 30 | 0 |
| Eggs (12) | Markets | 12 | 0 |
| Fresh Cheese (1kg) | Markets | 12 | 0 |
| Water (1.5 liter bottle) | Markets | 30 | 0 |
| Bottle of Wine (Mid-Range) | Markets | 4 | 0 |
| Domestic Beer (0.5 liter bottle) | Markets | 6 | 0 |
| Imported Beer (0.33 liter bottle) | Markets | 6 | 0 |
| Pack of Cigarettes (Marlboro) | Markets | 15 | 0 |
| One-way Ticket (local transport) | Transportation | 20 | 0 |
| Monthly Pass | Transportation | 1.5 | 0 |
| Apples (1kg) | Markets | 20 | 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 (3 bedrooms) in City Centre | Rent Per Month | 0 | 0.25 |
| Apartment (3 bedrooms) Outside of Centre | Rent Per Month | 0 | 0.25 |
| Basic (Electricity, Gas, Water, Garbage) | Utilities (Monthly) | 1 | 0 |
| 1 min. of Prepaid Mobile Tariff (no discounts or plans) | Utilities (Monthly) | 600 | 0 |
| Internet (6 Mbps, Flat Rate, Cable/ADSL) | 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 | Salaries And Financing | 0 | 0 |
| Gasoline (1 liter) | Transportation | 80 | 0 |
| Fitness Club, Monthly Fee for 1 Adult | Sports And Leisure | 2.3 | 0 |
| Tennis Court Rent (1 Hour on Weekend) | Sports And Leisure | 5 | 0 |
| Cinema, International Release, 1 Seat | Sports And Leisure | 7 | 0 |
| Chicken Breasts (Boneless, Skinless), (1kg) | Markets | 20 | 0 |
| Volkswagen Golf 1.4 90 KW Trendline (Or Equivalent New Car) | Transportation | 0.008 | 0 |
| 1 Pair of Levis 501 (Or Equivalent) | Clothing And Shoes | 0.6 | 0 |
| 1 Summer Dress in a Chain Store (Zara, H&M, ...) | Clothing And Shoes | 0.6 | 0 |
| 1 Pair of Nike Shoes | Clothing And Shoes | 0.6 | 0 |
| 1 Pair of Men Leather Shoes | Clothing And Shoes | 0.6 | 0 |
| Taxi Start (Normal Tariff) | Transportation | 12 | 0 |
| Taxi 1km (Normal Tariff) | Transportation | 60 | 0 |
| Taxi 1hour Waiting (Normal Tariff) | Transportation | 0 | 0 |
| Oranges (1kg) | Markets | 20 | 0 |
| Potato (1kg) | Markets | 20 | 0 |
| Lettuce (1 head) | Markets | 12 | 0 |
| Cappuccino (regular) | Restaurants | 30 | 0 |
+-------------------------------------------------------------+------------------------+------------+-------------+
47 rows in set (0.00 sec)
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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