Elisabetta Ottoz (p), Piermassimo Pavese , Lisa Sella

John Östh

In the last three decades European cities have been affected by a particular type of noise pollution stemming from recreational activities generally located in the city centers, the so-called movida.
The adverse environment for an apartment located in a “movida” district will result in a lower market value as compared to an apartment with similar characteristics, except for recreational noise. This occurs because potential buyers reduce their demand, as they discount present
value of the costs of annoyance, loss of tranquility, and possible health effects. A measure of the noise-induced damages is the difference between the market-determined values of the two apartments.
On the contrary, some claim that the price per square metre of properties has increased after “movida” developed, at least in certain districts, which were previously dilapidated and where nighttime economy was seen as a means to restart an area. Patrigest, an Italian company specialized in Valuation and Advisory for real estate, conducted in 2011 a research in Rome and Milan reaching the conclusion that excessive recreational noise depreciates the real estate’s value by 10 to 20% .
The two statements are not necessarily contradictory because we face a fragmented situation where two neighbors may report very distant night experiences, according, for instance, to the location of bedrooms.
If bedrooms overlook a protected courtyard, the estate is rather quiet and inhabitants may not suffer from noise pollution, but enjoy the lively atmosphere at no environmental costs.

Specifically, we address the following two research questions:
1. Do housing market responses to the noise pollution from recreational activities evidenced in previous studies from urban rail or traffic noise?
2. What are the underlying mechanisms that drive heterogeneity in housing market responses?

The aim of this paper is to examine the effect of recreation noise on house prices. We use an original highly detailed housing transactions dataset from the City of Turin covering the period from 2017 to 2018. Using data on all recreational activities placed in the Turin in 2018, we calculate the distance from every property that is sold to the nearest commercial activities. A property represents not only an amount of structural characteristics, but also a set of specific locational characteristics. Disamenities have a negative effect on house price, which means people have a low demand and do not have the willingness to pay more for these characteristics and we use hedonic model urban to estimate this discount.

Peter Haller (p), Matthias Dorner

John Östh

With over four million cases in Germany every year, influenza and infectious diseases of the
respiratory tract (henceforth ARD) have the highest number of reported doctor consultations.
Although the direct treatment costs are comparably low, the indirect effects, due to work
absence, are far more compelling. In this paper, we estimate the effect of local ARD diseases
as an exogenous shock to the production factor labor and thus on levels of firm productivity.
To quantify the ARD related shock on the production factor labor, we geocode maps of weekly
reported flu emergence in Germany from official influenza surveillance data. Measured by the
length of the influenza season in German municipalities, these data exhibit substantial seasonal
variation as well as high variance across regions. In our main analysis, we estimate firm-level
production functions using data from a comprehensive German firm survey. In our main
regression, we analyze total factor productivity differentials and their relationship with the local
influenza intensity. First results show sizeable negative effects of the ARD diseases and the flu
on firm productivity.

Katarzyna Kopczewska (p), Piotr Ćwiakowski , Mateusz Kopyt

John Östh

Immensely reach menu of theories and empirical evidence in regional and urban studies is still missing a link between business and residential location within city, with special insight into firms profitability and housing valuation. Existing theories of location of both business and housing, together with their inherent agglomeration and spatial externalities effects, have in fact the same drivers and deserve for being considered jointly. Common for both starting point is the decision process, where to live and where to locate the firm, as well as what are the factors which determine this location. Independently of the adopted research perspective and internal motivations of the decision-takers, one observes its final consequences as the densities of business and housing, as well as its economic performance – prices of real estate and profitability of business.
The intersection of both streams, the business and housing location and valuation, lies in the similar determinants and the consequences of both processes, where one can indicate clustering, density, agglomeration externalities, Central Business District location, rural vs. urban sites, accessibility, proximity to important neighbours, quality and usefulness of neighbourhood, absolute and relative locations, local and global perspective, spatial segregation etc. Both processes are also inter-connected.
Overview of existing literature on location models for housing and business does not offer the explanation to patterns and interactions observed in nature. Assumptions and analytical framework in neoclassical New Economic Geography and Institutional Economic Geography are to fail: both rational utility/profit maximizing agents as well as “rules & communication” driven agents. An attractive theoretical framework for this problem is co-evolutionary economic geography, which may serve as integrating umbrella cross over the agents and mechanisms involved.
This paper compares the theories of business and housing location and performance with special insight into spatial externalities to show what elements are common. Neither of old models is out-of-date, while its joint application may shed some new light into interactions which appear.
This builds the fundamentals for statistical analysis of those two processes, till now treated in general separately. We compare the existing models and studies in both streams and check empirically if spatial distributions of companies and their profits are linked to spatial distributions of the flats and their observed prices. We hypothesise that there exists visible links and spatial interactions between both. In an empirical illustration we use geo-located point big data for firms and flats in Warsaw, Poland in 2016.