Regional exposure model

An open and transparent residential building inventory for the Andean countries has been developed. The model captures the location and distribution of dwellings and buildings along with structural characteristics, which include information about construction materials, lateral load resisting system, range of number of storeys, average built area, replacement cost, and population. The following sections describe the assumptions and methodology utilised to develop the inventory, which was based on open census data and expert opinion.

The exposure model was developed following four main steps:

1. Definition of building classes, which includes description of the assumptions and limitations for defining the existing building classes in the region.
2. Estimation of dwelling fractions based on census information and inferred mapping schemes.
3. Estimation of number of buildings (rather than dwellings) based on expert judgment.
4. Estimation of replacement cost based on reference values.

Thirteen inland countries constitute South America. Its population estimated in 2012 overcomes 400 million of inhabitants, where almost 200 million are located in the Andean region, one of the most seismic active zones in the world. The dwellings in the Andean countries have been estimated in 47.3 million units, which correspond to 30.4 million buildings, with a built-up area of 3,621 million square meters, a total replacement cost of 1,554 USD billions, and 175.6 million inhabitants. The table and figure below present a summary of the regional results.

Country exposure model	Distribution of the building inventory in the Andean Region
Argentina
Bolivia
Chile
Colombia
Ecuador
Peru
Venezuela

CLICK HERE to download exposure models in *.csv or NRML (OpenQuake) format

Regarding the spatial distribution of the residential building inventory, the bottom figure on the left shows the number of dwellings, buildings and replacement cost in each country, along with pie charts that represent the national dwelling fractions (urban plus rural). The figure on the right dwelling distribution at the first level subdivision.

A summary of the materials and typologies found in the seven selected countries is presented in the following figures. It can be observed that building fractions change considerably from country to country and depending on the type of region: urban or rural. figure a illustrates the predominant urban dwelling fractions in each country, while Figure b shows the aggregated urban fractions at regional level; figure c and d illustrate similar results but for rural areas.

Note that the results presented on the figures have been aggregated into a number of macro building classes for the sake of clarity; however the complete exposure model in each country contains forty building types.

At regional scale the pie chart bellow shows the contribution of each building type (urban + rural areas) to the that total building stock. Masonry construction represent 55% of the exposed portfolio (being the main typologies unreinforced, confined and reinforced masonry that represent 31%, 22% and 2% respectively); followed by 17% of reinforced concrete buildings (14% of moment frames with or without infill walls and 3% of dual or wall systems), 13% of earth/adobe houses and 8% wooden structures. The remaining 7% is distributed amongst steel, stone and unknown typologies (1%, 2% and 4% respectively).

Trends in population for the Andean countries were also investigated. The figure below on the left shows the population distribution in the region with pie charts that indicate the proportion of dwellings in urban (light grey) and rural (dark grey) areas, while the figure on the right shows the spatial distribution of the total population at the first level. The average number of occupants per dwelling in the region is 3.8. Peru and Colombia have the largest occupants rate (4.3 and 4.2 respectively), while Argentina and Bolivia have the lowest (2.9 and 3.6 respectively).

The study has identify that the Andean countries are characterised by large urban concentration of population and buildings; in fact, it was found that 50% of the building inventory is located in only 15 regions, as presented in Table bellow.

The table above also presents the peak ground acceleration (PGA) at the 10% probability of exceedance in 50 years (on bedrock) for the main cities in accordance with the latest hazard maps of the building codes. Most of the seismic regulations consider that PGA values larger than 0.25g are related to high seismicity zones, which allocates the cities of Cali, Quito, Guayaquil, Lima, Santiago, Valencia and Caracas in the high seismic hazard category. These cities account for 17.4% of the total dwellings in the Andean countries. Similarly, low seismic zones are associated with PGA values lower than 0.10g, which includes the regions from Argentina and Bolivia, representing approximately 22.6% of the total dwellings.

The building stock has been classified according to a set of building classes that indicate the structural characteristics and expected performance under seismic action. In order to identify the main building classes in the Andean region, a review of existing classifications was conducted. These include the housing reports from the World Housing Encyclopedia (WHE), and the building fractions available in the Global Exposure Database (GED) at national level, that comprise results from PAGER and UN-HABITAT studies. Moreover, regional experts strongly contributed to the definition of the building classes.

For the present study, residential building classes were classified based on the lateral load resisting system and its material, the ductility level and the range of number of stories.

The forty building classes considered in the project are summarised in the table. These classes define a comprehensive set of buildings in the region, and they can be easily collapsed into less categories depending on their representativeness within each country and/or on the availability of fragility/vulnerability functions for each building class.

Note that the building classes have been classified using the GEM building taxonomy (Brevz et al. 2013), in order to provide a uniform and consistent taxonomy.

Two regional workshops were organized in March 2014 in Medellin, Colombia (download report Medellin-Colombia), and May 2015 in Lima, Peru (download report Lima-Peru), with experts from the various Andean countries. These events promoted the discussion between the representatives of the different countries, and allowed sharing experiences and data regarding exposure, vulnerability and results from previous risk studies.

...see more details about building classes

Population and housing statistics usually provide information regarding number of dwellings and physical housing attributes that exist in a given area, and not the number of buildings, which are required in exposure models as further explained in the next section. Moreover, the information that is used to describe each dwelling in the census varies across the different countries, and may not cover all of the features required to characterize a structure according to its seismic performance.

For quantifying the number of dwellings, mapping schemes that provides dwelling fractions of each building type according to census information were used in each country for urban and rural areas. The proposed dwelling fractions were estimated based on regional collaboration and the opinion of more than 20 experts from the region.

Dwelling fractions at national level are summarised in the table, where the main five typologies in each country are highlighted in light grey.

...see more details about dwelling fractions

In order to perform an economic loss or damage assessment, it is necessary to quantify the number of buildings, rather than number of dwellings. For the present model, the number building was computed by multiplying the number of dwellings times the average number of dwellings per story times the average number of storeys.

The dwelling fractions computed in previous sections include the range of number of storeys for each building type, and through expert judgment the average number of storeys in each typology and the average number of dwellings per story were defined. The following table presents a summary of the assumed values.

The final step is the estimation of the replacement cost per building type, which differs from the exposed value. In this context, the replacement cost refers to the value of replacing a building in accordance with the latest building standards applicable for the country, and it includes the cost of the lateral load resisting system and the non-structural components (the cost of the land is not included). For instance, in the case of an unreinforced masonry house, the replacement cost will be the value of building a confined or reinforced masonry structure, because actual codes do not allow the construction of unreinforced masonry houses due to their poor seismic performance.

Since construction cost is commonly found per meter squared of dwelling, then the average floor area per dwelling type is required. In this case, instead of assigning an average area to each building type, three quality categories were selected: upper, middle and lower, and each building type was assigned to a quality category, see table bellow. The same methodology was applied to structural replacement cost.

The following table presents the average area per dwelling and the average replacement cost per built area utilised in the model, which were estimated base on local research and reference values available in the WHE housing reports (indicated in the results of each country). It can be observed that a unique national value was proposed, which is a rough approximation considering that within a country the replacement cost varies considerably from region to region, as well as from urban to rural areas, but it was decided to assume it constant until additional research is carried out.

Finally, given that each country has its own currency, US dollars was selected as the reference currency in order to homogenise and compare values among countries. In particular, the reference cost values presented in Table 4.4 for Venezuela and Argentina are very variable, since these countries currently have significant variation in the inflation, and official currency rates are far from the real situation inside the countries.