Questioni Di Economia E Finanza Banca D Italia-Books Pdf

Questioni di Economia e Finanza Banca D Italia
11 Apr 2020 | 22 views | 0 downloads | 33 Pages | 1.23 MB

Share Pdf : Questioni Di Economia E Finanza Banca D Italia

Download and Preview : Questioni Di Economia E Finanza Banca D Italia


Report CopyRight/DMCA Form For : Questioni Di Economia E Finanza Banca D Italia



Transcription

Questioni di Economia e Finanza, Occasional Papers. Forecasting house prices in Italy, by Simone Emiliozzi Elisa Guglielminetti and Michele Loberto. Number 463 October 2018, The series Occasional Papers presents studies and documents on issues pertaining to. the institutional tasks of the Bank of Italy and the Eurosystem The Occasional Papers appear. alongside the Working Papers series which are specifically aimed at providing original contributions. to economic research, The Occasional Papers include studies conducted within the Bank of Italy sometimes. in cooperation with the Eurosystem or other institutions The views expressed in the studies are those of. the authors and do not involve the responsibility of the institutions to which they belong. The series is available online at www bancaditalia it. ISSN 1972 6627 print, ISSN 1972 6643 online, Printed by the Printing and Publishing Division of the Bank of Italy.
FORECASTING HOUSE PRICES IN ITALY, by Simone Emiliozzi Elisa Guglielminetti and Michele Loberto. Forecasting house prices is a difficult task given the strong relationship between real estate. markets economic activity and financial stability but it is an important one This paper. evaluates the out of sample forecasting performance of various models of house prices in a. quasi real time setting Focusing on Italy we consider two structural models using. simultaneous equations and a Bayesian VAR and compute both conditional and. unconditional forecasts We find that the models perform better than a simple autoregressive. benchmark however the relative forecast accuracy depends on the forecast horizon and also. changes over time For the full sample period the simultaneous equation model which takes. into account credit supply restrictions and real estate taxation shows the best performance. measured in terms of root mean squared forecasting error RMSFE In the first part of the. sample 2005 2010 medium term forecasts of house prices greatly benefit from conditioning. on the evolution of households disposable income whereas from 2010 onwards the path of. the stock of mortgages becomes important, JEL Classification C32 C53 E37 R39. Keywords house prices forecasting structural model BVAR. Introduction 5, 1 Literature review 7, 2 Data and stylized facts 9. 3 Models 13, 3 1 Structural models 13, 3 2 BVAR model 14. 4 Quasi real time forecasting design and out of sample evaluation 16. 4 1 Whole sample exercise 2005Q1 2016Q4 16, 4 2 First subsample exercise 2005Q1 2010Q4 21.
4 3 Second subsample exercise 2011Q1 2016Q4 23, 5 Conclusions 25. Appendix 26, References 28, Bank of Italy Directorate General for Economics Statistics and Research. Introduction, Following the global financial crisis much more attention has been devoted to the link between. housing markets and the macroeconomy Dwellings are the main source of household wealth and. by affecting its value house prices have an impact on household consumption Mian et al 2013. House prices are also relevant for activity in the construction sector when house prices increase. firms find more profitable to build more dwellings Glaeser and Gyourko 2005 supporting via this. channel also total employment and households disposable income Finally the evolution of house. prices is important also for the role of housing as collateral both for households and small firms. Banerjee and Blickle 2016, Given these links reliable forecasts of house prices are crucial for the assessment of the. macroeconomic outlook and for the evaluation of potential risks to financial stability arising in the. housing market In this paper we consider and compare two different approaches to predict house. prices a structural approach simultaneous equations and a Bayesian vector autoregression model. BVAR In our view the first methodology is the best tool to obtain medium term forecasts based. on internally consistent stories Structural models gives the opportunity to understand how. different channels affect the housing market they thus allow to perform scenario analyses and to. test their responsiveness to changes in key variables Overall these features are particularly. important for forecasting house prices and for assessing the risks to financial stability the multifold. interactions between the housing market credit markets and the overall macroeconomic activity. must be taken into account Differently the BVAR is a reduced form model that in many empirical. applications achieves a superior forecasting performance when compared to alternative approaches. Doan Litterman and Sims 1984 Karlsson 2013 it can thus be regarded as a strong competitor of. the structural models When using the BVAR approach we test the performance of both. unconditional and conditional forecasts as in Banbura Giannone and Lenza 2015 In summary. structural models are more suitable for policy purposes since they allow for a storytelling which. rationalizes the forecasts however this comes at the risk of model misspecification when one. imposes constraints which do not hold in the data On the contrary BVARs are flexible and. parsimonious and may thus prove superior in forecasting when the reduced form relationships. between the data are sufficient to characterize their evolution over time Clearly the economic. interpretation of the forecasting exercise is rather limited instead 1. A complementary approach would be represented by structural BVARs in which the analyst imposes restrictions to. identify some shocks of interest this would allow an economic interpretation of house price dynamics In this work. We consider two structural models presented in Loberto and Zollino 2016 and Nobili and. Zollino 2017 such models consist of three blocks of equations each of them describing the. equilibrium in the market for dwellings for mortgage loans to households and for loans to. construction firms respectively Loberto and Zollino 2016 further takes into account credit supply. restrictions and taxation on housing, The BVAR estimation follows Giannone Lenza and Primiceri 2015 who propose a new.
methodology for setting the informativeness of the prior for the model coefficients based on. Bayesian hierarchical modeling see Appendix B, All models are estimated using samples starting in 1986Q1 with an expanding window2 while. their forecasting performance is assessed using a recursive exercise in quasi real time i e using the. last vintage of data on a period spanning from 2005Q1 up to 2016Q4 and looking to a projection. horizon from 1 to 12 quarters ahead, The first result is that all the models are useful in predicting house price dynamics and pass an. important test Breitung and Knuppel 2017 for all the horizons the root mean squared forecasting. error RMSFE is lower than the unconditional standard deviation of the house price index 3. Second for horizons shorter than one year all models have a similar predictive accuracy and there is. no clear winner For the medium run between one and three years the forecasts of the structural. models and those of the conditional BVAR have a superior accuracy with respect to the. unconditional BVAR indicating that at longer horizons house prices are strongly influenced by. macroeconomic determinants Lastly a sub sample analysis reveals that in order to have good. projections for the Italian house price index before 2010 it is enough to condition on disposable. income conversely in the most recent years it is more important to further condition on the growth. rate of the stock of mortgages to avoid systematic over predictions of house prices during the crisis. The rest of the paper is structured as follows Section 1 reviews the related literature Section 2. presents the data and the stylized facts Section 3 describes the models Section 4 illustrates the. forecasting exercises and the results Section 5 concludes. however we prefer to fully exploit the flexibility of the reduced form BVARs against the tight structure imposed by the. simultaneous equation models, The first estimation sample common to all models considered in the analysis ranges from 1986Q1 till 2004Q4 so that. the out of sample exercise starts in 2005Q1, When the RMSFE is higher than the unconditional standard deviation of the target variable the forecasting model is. totally misspecified, 1 Literature review, In this work we adopt a macroeconomic perspective to forecasting residential property prices Since.
the outbreak of the Global Financial Crisis the attention of central banks on modeling and. forecasting the evolution of real estate variables has increased substantially because of their. fundamental role in the assessment of macroeconomic and financial stability Indeed. macroprudential measures have been adopted by several European countries following the. recommendations of the European Systemic Risk Board ESRB about the vulnerabilities arising. from the real estate market 4, For the ECB and the national central banks the evaluation of the accuracy of house price forecasts. is of primary importance given their use in the Eurosystem staff projections stress testing exercises. and the Financial Stability Report FSR which gauges the resilience of the whole financial system. that is strongly interconnected with the real estate sector The evolution of real estate markets is. thus regularly and closely monitored also future dynamics of house prices are considered. consistently with the broad macroeconomic scenario. Forecasting house prices is a challenging task for a variety of reasons. The first one is related to data availability long time series of house prices with a reasonable. coverage of the whole national market are relatively scant especially for European countries In. addition the data may capture different phenomena depending on the construction of the index and. the aggregation method a relevant issue as explained in Section 2 is how to take into account. dwellings heterogeneity and changes in the quality of houses put on sale Moreover house price. indexes are generally released with significant delays with respect to the reference period. As pointed out by Ghysels et al 2013 only few works have been able to study out of sample. OOS henceforth forecast accuracy of house prices because of limited availability of long time. series In this work we can go one step further our Italian house price index which is representative. of the Italian real estate market starts in 1986 and is computed at quarterly frequency allowing us. to rank the models based on OOS statistics Since the Italian Statistical Institute ISTAT publishes. a quarterly house price index based on actual transactions that starts in 2010 we use the. reconstruction made by Muzzicato et al 2008 that extends it back in time till 1986 based on. average unit values per squared meters, ESRB Vulnerabilities in the EU residential real estate sector November 2016. The second difficulty in forecasting house prices emerges because the real estate market has wide. and strong connections with the rest of the economy but their relative importance may change over. time Demand factors such as disposable income interest rate on households mortgages and the. flow of household mortgages are usually assumed to play the most important role with the supply. of housing being relatively inelastic to market conditions However some contributions to the. literature have stressed the importance of supply side factors as well Strauss 2012 finds that. building permits improve the predictions of construction volumes and prices while Spiegel 2001. shows in a theoretical model that construction cycles may arise in presence of credit constraints. Furthermore there is no consensus in the literature on the importance of credit for house price. forecasting Many analyses find a strong positive effect of credit conditions on residential property. prices Igan and Loungali 2012 Goodhart and Hofmann 2008 Annett 2005 and Tsatsaronis and. Zhu 2004 However Goodhart and Hofmann 2008 and Simigiannis and Hondroyiannis 2009. highlight the problem of reverse causality which means that bank credit is itself driven by favorable. conditions in the real estate market Moreover Annett 2005 shows that the relationship between. credit and house prices is significant only in the long run whereas Gerdesmeier et al 2011 find. asymmetric effects depending on the state of the economy This relationship may also be shaped by. institutional characteristics irrespective of the real economic outlook Mian and Sufi 2011 Our. work is agnostic in this perspective since we rely on several approaches that can accommodate. different views the structural models take into account supply demand and credit factors by. imposing equilibrium relationships whereas the BVARs are more parsimonious and capture only. demand and financing conditions without any restriction on the short and long run dependency. between credit and house prices The model in Loberto and Zollino 2016 also considers credit. supply restrictions and changes in property taxation Consistent with the literature on house prices. momentum all the models we consider exploit the autocorrelation structure in the dependent. variable We do not explore the causes of the persistence in house price dynamics however several. explanations have been provided by the theoretical literature ranging from downpayment. constraints Stein 1995 agency problems which affect banks risk taking behavior Allen and. their forecasting performance is assessed using a recursive exercise in quasi real time i e using the last vintage of data on a period spanning from 2005Q1 up to 2016Q4 and looking to a projection horizon from 1 to 12 quarters ahead The first result is that all the models are useful in predicting house price dynamics and pass an

Related Books

PENGEMBANGAN METODE PEMBELAJARAN PENDIDIKAN KARAKTER

PENGEMBANGAN METODE PEMBELAJARAN PENDIDIKAN KARAKTER

MELALUI KEWIRAUSAHAAN SOSIAL SOCIOPRENEURSHIP Penny Rahmawaty Dyna Herlina Suwarto M Lies Endarwati Staf Pengajar Fakultas Ekonomi Universitas Negeri Yogyakarta penny rahmawaty yahoo com dynaherlina yahoo com lies endarwati yahoo com Abstrak Penelitian ini bertujuan untuk mengembangkan model pembelajaran pendidikan karakter dalam pembelajaran kewirausahaan sosial melalui implementasi nilai

PERAN UMKM DALAM PENGEMBANGAN TECHNOPRENEURSHIP DI

PERAN UMKM DALAM PENGEMBANGAN TECHNOPRENEURSHIP DI

yang merupakan implementasi dari SCL dan RBL dirasa sesuai dengan tujuan tersebut Melalui pola ini secara paralel perguruan tinggi juga dapat berperan dalam menyelesaikan permasalahan permasalahan yang dihadapi oleh masyarakat sekitarnya dalam hal ini UMKM Keterlibatan UMKM sebagai basis dalam perancangan produk perlu dilakukan secara terus menerus Hal ini diyakini mampu memicu

DEWAN REDAKSI karyailmiah unipasby ac id

DEWAN REDAKSI karyailmiah unipasby ac id

DEWAN REDAKSI Pengarah Titi Rapini SE MM Dra Umi Farida M M Ak CA Dra Hj Khusnatul Zulfa Choirul Hamidah SE MM Penanggung Jawab Sri Hartono SE MM

MULTI FUNCTION CONCEPT TO SUPPORT INDEPENDENT VILLAGE

MULTI FUNCTION CONCEPT TO SUPPORT INDEPENDENT VILLAGE

ENERGI BERBASIS SOCIOPRENEURSHIP KARYA ILMIAH YANG DIAJUKAN UNTUK MENGIKUTI PEMILIHAN MAHASISWA BERPRESTASI TINGKAT NASIONAL OLEH DITA FOMARA TUASIKAL NIM 201610160311094 JURUSAN MANAJEMEN FAKULTAS EKONOMI DAN BISNIS UNIVERSITAS MUHAMMADIYAH MALANG MALANG 2019 ii LEMBAR PENGESAHAN Judul Karya Tulis Multi Function Concept to Support Independent Village Optimalisasi Potensi Sagu Sebagai

PEMBERDAYAAN MASYARAKAT MELALUI PENDEKATAN SOCIOPRENEURSHIP

PEMBERDAYAAN MASYARAKAT MELALUI PENDEKATAN SOCIOPRENEURSHIP

sociopreneurship has been done to empower the farmers and ranchers in rural communities Tirtonirmolo Kasihan Bantul Yogyakarta The effectiveness of this program has been proven that is expected to be transmitted to the rest of society Keywords sociopreneurship empowerment community I Pendahuluan Program pemberdayaan masyarakat telah dilakukan oleh pemerintah mulai dari pemerintah

What is 5S principle JICA

What is 5S principle JICA

What is 5S principle 5S Training of Trainers for Training Institutions Training material No 13 Aren t you frustrated in your workplace I cannot remember what how to next Why I am making mistakes again and again Oh this position makes me tired Where is that document I cannot find it Oh time is not enough to complete this work Why we cannot communicate properly Are you

Solution methods for the Incompressible Navier Stokes

Solution methods for the Incompressible Navier Stokes

Incompressible Navier Stokes Equations Mom Equations Reference Quantities Non dimensional Eqn Reynolds and Strouhal s ME469B 3 GI 18 Implicit scheme for steady NS equations Compute an intermediate velocity field eqns are STILL non linear Define a velocity and a pressure correction Using the definition and combining Derive an equation for u ME469B 3 GI 19 Implicit scheme for

PUBLIC PAY RESOURCE GUIDE Streamhoster com

PUBLIC PAY RESOURCE GUIDE Streamhoster com

At A Place for Mom we know that the cost of senior care can stretch family budgets to the breaking point We also know that as difficult as it can be to get a true picture of a loved one s needs and then match him

Parent Reference Guide to 6th Grade Math

Parent Reference Guide to 6th Grade Math

Directions Simplify each expression 1 9 3 x 22 20 5 3 Groups are always number 1 6 x 22 20 5 3 Little Miss Exponent must be 2nd 6 x 4 20 5 3 Mom and Dad Whoever is on the left 24 20 5 3 will go first 24 4 3 Addison and Sister Whoever is on the left

JOURNAL OF LA A Projector based Movable Hand held Display

JOURNAL OF LA A Projector based Movable Hand held Display

A Projector based Movable Hand held Display System for Interactive 3D Model Exhibition Authors Abstract Traditional display systems usually display objects on static screens monitors walls etc and the interaction between the displaying object and the viewer can only be via keyboard and mouse It will be attractive if we display the object to a hand held screen and interact with it using

Palaeontologia Electronica University of Colorado

Palaeontologia Electronica University of Colorado

tal elements under load Rayfield et al 2001 Man ning et al 2006 Sereno et al 2007 and imaging techniques such as LIDAR are powerful tools in the analysis of fossil trackways of vertebrates Bates et al 2008 However the most directly applicable technique is locomotor modelling Models vary from the highly theoretical e g Alexander