Skip to main content
Log in

Macroeconomic implications of mortgage loan requirements: an agent-based approach

  • Regular Article
  • Published:
Journal of Economic Interaction and Coordination Aims and scope Submit manuscript

Abstract

It is a well-known fact that the housing market, with its associated mortgage securities, plays a crucial role in modern economies. The recent crisis of 2007, triggered by the U.S. real estate bubble, confirms this key role and suggests the importance of regulating mortgage lending. This paper investigates these issues by designing a housing market with a linked mortgage lending instrument in the Eurace agent-based model. Our results show that the presence of a housing market in the model has relevant macroeconomic implications, driven mainly by the additional amount of endogenous money injected into the economy by new mortgages. This additional money generally helps to support and stabilize aggregated demand, thus improving the main economic indicators. However, if the regulation of mortgage lending is too lax, involving an increase in the debt-service-to-income ratio (DSTI), then the additional supply of mortgages no longer enhances macroeconomic performance, and the stability of the economic system is undermined. Based on a number of recent discussions, a regulation of stock control that targets households’ net wealth (a stock), rather than income (a flow) is designed and analyzed. The results show that regulation of stock control can be combined effectively with DSTI to increase the stability of the housing market and the economy as a whole. Interestingly, the regulation based on stock control also directly affects mortgage distribution among households, avoiding excessive concentration. From a policy perspective, our results suggest that the use of a mild flow control regulation, coupled with a stricter stock control measure, fosters sustainable growth and eases first-time buyers access to the housing market, encouraging homeownership.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. DSTI is often defined simply as the Debt-Service-Ratio (DSR).

  2. The British parliament approved regulations granting powers to the Financial Policy Committee over housing tools, and specifically regulating residential mortgage lending using LTV and DTI ratios on March 25, 2015. These measures came into force on April 6, 2015.

  3. See the speech entitled “The household debt ratio is an unsuitable risk measure - there are much better ones” delivered by Lars E.O. Svensson, a Resident Scholar at IMF (https://larseosvensson.se/2014/05/19/the-household-debt-ratio-is-an-unsuitable-risk-measure-there-are-much-better-ones/).

  4. A calendar month is defined as a set of 20 days; a calendar week is five days.

  5. This provision is aimed smoothing the production plan over time and then reducing oscillations of input demand.

  6. The Cobb-Douglas production function is used widely in economics literature and has been employed in the base Eurace model that we extended in this study. However, the Cobb-Douglas function has often been criticized for being theoretically weak and unrealistic [see Shaikh (1974) and Labini (1995) among the many]. Replacing the Cobb-Douglas production function with the Leontief or other production functions may possibly be considered for the Eurace model, along with a dedicated study on comparison of different production functions under an ABM simulation environment.

  7. If available liquidity is not even sufficient to meet compulsory payments, i.e. debt service and taxes, then the firm enters a process called illiquidity bankruptcy, where the firm fires all workers and remains inactive until it is able to raise the necessary funds on the stock market.

  8. In particular, each firm updates the value of its net worth, and if equity becomes negative, the firm is declared insolvent. In that case, it enters a special process called insolvency bankruptcy, where the firm fires all workers, undergoes debt restructuring with a related loan write-off and a corresponding equity loss on creditor banks’ balance sheets, and remains inactive for a period of time, after which it re-enters the market with a healthy balance sheet. Physical capital of insolvent firms is therefore not lost, but simply remains inactive for a while.

  9. The reservation wage is set at the last received wage, and is then heterogeneous among households.

  10. Unemployment benefit is set at a fraction \(\xi _U\) of the last salary received by the household.

  11. The transfer payment is set at a fraction \(\xi _T\) of the average wage among households.

  12. The liquid wealth is given liquidity plus the market value of the stocks and government bonds portfolio.

  13. The number of public employees is set at a fixed percentage \(\xi _G\) of the total household population.

  14. Tables 4 and 5 in the “Appendix” show a household’s income and its wealth.

  15. Please note that in each boxplot shown in this paper, the line dividing each box into two halves is the median value, where each half corresponds to a quartile of the underlining distribution. The diamond-shaped point denotes the mean of the distribution. All observations from the entire runtime of each simulation seed are used to calculate the values that determine the shape of the boxplot, and hence the distribution of the variable observed.

  16. See, for instance, the Bank of England Quarterly Bulletin 2014 Q1.

  17. See (Muellbauer 2012). This demonstrates that housing wealth does not have a very strong impact on the consumption level. The study is based on data from developed countries.

  18. Household consumption depends on a precautionary saving motivation, determined by a target ratio \(\omega _C\) of liquid wealth \(W_h\) to total net income \(y_h^{net}\) (see Eq. 5).

  19. For a recent overview, see the box “Stylized Facts of Money” in ECB (2013).

References

  • Anderson G, Bunn P, Pugh A, Uluc A (2014) The potential impact of higher interest rates on the household sector: evidence from the 2014 NMG Consulting survey. Bank England Q Bull 54(4):419–433

    Google Scholar 

  • Axtell R, Farmer D, Geanakoplos J, Carella E, Conlee B, Goldstein J, Hendrey M, Kalikman P, Masad D, Palmer N, Yang Cy (2014) An agent-based model of the housing market bubble in metropolitan Washington, DC Tech. rep., Deutsche bundesbank spring conferences on housing markets and the macroeconomy

  • Baffoe-Bonnie J (1998) The dynamic impact of macroeconomic aggregates on housing prices and stock of houses: a national and regional analysis. J Real Estate Finance Econ 17(2):179–197

    Article  Google Scholar 

  • Baptista R, Farmer JD, Hinterschweiger M, Low K, Tang D, Uluc A (2016) Macroprudential policy in an agent-based model of the UK housing market. Bank England, Staff Working Paper No 619, pp 1–50

  • Battiston S, Farmer JD, Flache A, Garlaschelli D, Haldane AG, Heesterbeek H, Hommes C, Jaeger C, May R, Scheffer M (2016) Complexity theory and financial regulation. Science 351(6275):818–819

    Article  Google Scholar 

  • Benartzi S, Thaler RH (1995) Myopic loss aversion and the equity premium puzzle. Q J Econ 110(1):73–92

    Article  Google Scholar 

  • Bjarnason T, Erlingsson EJ, Ozel B, Stefansson H, Sturluson JT, Raberto M (2015) Macroeconomic effects of varied mortgage instruments studied using agent-based model simulations. Tech. rep., working papers 2017/10, Economics Department, Universitat Jaume I, Castellon (Spain)

  • Blum J, Hellwig M (1995) The macroeconomic implications of capital adequacy requirements for banks. Eur Econ Rev 39(3):739–749

    Article  Google Scholar 

  • Borsch-Supan A (1994) Housing market regulations and housing market performance in the United States, Germany, and Japan. In: Social protection versus economic flexibility: is there a trade-off?, University of Chicago Press

  • Bubb R, Krishnamurthy P (2015) Regulating against bubbles: how mortgage regulation can keep main street and wall street safe–from themselves. SSRN scholarly paper ID 2558110, Social Science Research Network, Rochester, NY

  • Carroll CD (2001) A theory of the consumption function, with and without liquidity constraints. J Econ Perspect 15(3):23–45

    Article  Google Scholar 

  • Case KE, Glaeser EL, Quigley JM (eds) (2009) Housing markets and the economy: risk, regulation, and policy: essays in honor of Karl E. Case. Lincoln Institute of Land Policy, Cambridge, MA

  • Catte P, Girouard N, Price R, André C (2005) The contribution of housing markets to cyclical resilience. OECD Econ Stud 2004(1):125–156

    Article  Google Scholar 

  • Caverzasi E, Godin A (2015) Post-keynesian stock-flow-consistent modelling: a survey. Camb J Econ 39(1):157–187

    Article  Google Scholar 

  • Cincotti S, Raberto M, Teglio A (2010) Credit money and macroeconomic instability in the agent-based model and simulator Eurace. Econ Op Access Op Assess E-J 4(2010-26):1–32

  • Dagher J, Fu N (2011) What fuels the boom drives the bust; regulation and the mortgage crisis. IMF working paper 11/215, international monetary fund

  • Dawid H, Gemkow S, Harting P, Kabus K, Neugart M, Wersching K (2008) Skills, innovation, and growth: an agent-based policy analysis. J Econ Stat 228(2+3):251–275

    Google Scholar 

  • Deaton A (1992) Household saving in ldcs: credit markets, insurance and welfare. Scand J Econ 94(2):253–273

    Article  Google Scholar 

  • Deloitte (2014) Property index overview of European residential markets: European housing 2013. Tech. rep., Deloitte Real Estate. 3rd edn

  • Dubecq S, Ghattassi I (2009) Consumption-wealth ratio and housing returns. Tech. Rep. 264, Banque de France

  • ECB (2013) Monthly Bulletin October, 2013. Tech. Rep. ISSN 1725-2822, European Central Bank

  • Erlingsson EJ, Teglio A, Cincotti S, Stefansson H, Sturluson JT, Raberto M (2014) Housing market bubbles and business cycles in an agent-based credit economy. Econ Op Access Op Assess E-J 8(2014-8):1–42

  • Erlingsson EJ, Ozel B, Teglio A, Stefansson H, Sturluson JT, Raberto M (2016) Wealth and income inequality dynamics andcredit rationing: an agent-based approach. Tech. rep., Universitat Jaume I

  • FI (2014) The swedish mortgage market 2014. Tech. rep, FinansInspektionen Mortgage Survey

  • FSA (2011) Mortgage Market Review: Proposed package of reforms. Tech. rep., The Financial Services Authority

  • Gallegati M, Greenwald B, Richiardi MG, Stiglitz EJ (2008) The asymmetric effect of diffusion processes: risk sharing and contagion. Glob Econ J 8(3)

  • Gareth A, Philip B, Pugh A (2014) Uluc A (2014) The potential impact of higher interest rates on the household sector: evidence from the 2014 NMG consulting survey. Bank of England Q Bull Q4:419–433

    Google Scholar 

  • Ge J (2014) Who creates housing bubbles? An agent-based study. In: Alam SJ, Parunak HVD (eds) Multi-agent-based simulation XIV, vol 8235. Springer, Berlin, pp 143–150

    Chapter  Google Scholar 

  • Gharaie E, Blismas N, Wakeield R (2012) Little’s law for the US house building industry. In: Tommelein ID, Pasquire CL (eds) 20th annual conference of the international group for lean construction. San Diego, USA

  • Gilbert N, Howksworth JC, Swinney PA (2009) An agent-based model of the English housing market. Technosocial Predictive Analytics, Papers from the 2009 AAAI Spring Symposium, Technical Report SS-09-09, Stanford, California, USA

  • Kydland FE, Rupert P, Sustek R (2012) Housing dynamics over the business cycle. NBER working paper 18432, National Bureau of Economic Research, Inc

  • Labini PS (1995) Why the interpretation of the cobb-douglas production function must be radically changed. Struct Change Econ Dyn 6(4):485–504 https://EconPapers.repec.org/RePEc:eee:streco:v:6:y:1995:i:4:p:485-504

  • Levin EJ, Wright RE (1997) The impact of speculation on house prices in the united kingdom. Econ Model 14(4):567–585

    Article  Google Scholar 

  • Meh CA, Terajima Y, Chen DX, Carter T (2009a) Household debt, assets, and income in canada: a microdata study. Tech. Rep. 2009-7, Bank of Canada

  • Meh CA, Terajime Y, Chen DX, Carter T (2009b) Household debt, assets, and income in canada: a microdata study. Tech. Rep. ISSN 1914-0558, Bank of Canada

  • Muellbauer J (2012) When is a housing market overheated enough to threaten stability? Tech. Rep. 623, University of Oxford, Department of Economics, discussion paper series

  • Muellbauer J, Murphy A (2008) Housing markets and the economy: the assessment. Oxford Rev Econ Policy 24(1):1–33

    Article  Google Scholar 

  • Raberto M, Teglio A, Cincotti S (2008) Prospect theory behavioral assumptions in an artificial financial economy. In: Schredelseker K (ed) Complexity and artificial markets. Lecture Notes in Economics and Mathematical Systems, vol 614, pp 55–66. Springer

  • Raberto M, Teglio A, Cincotti S (2012) Debt deleveraging and business cycles. An agent-based perspective. Econ Op Access, Op Assess E-J 6(2012-27):1–49

  • Richiardi M (2015) The future of agent-based modelling. Economics papers, economics group, Nuffield College, University of Oxford No 2015-W06

  • Rossi P (2008) L’offerta di mutui alle famiglie: caratteristiche, evoluzione e differenze territoriali. I risultati di un’indagine campionaria. Questioni di Economia e Finanza 13:1–28

    Google Scholar 

  • Santos JA (2001) Bank capital regulation in contemporary banking theory: a review of the literature. Financ Mark Inst Instrum 10(2):41–84

    Article  Google Scholar 

  • Saunders A, Allen L (2010) Credit risk measurement in and out of the financial crisis. Wiley, Hoboken

    Book  Google Scholar 

  • Shaikh A (1974) Laws of production and laws of algebra: the humbug production function. Rev Econ Stat 56(1):115–120

    Article  Google Scholar 

  • Skingsley C (2007) Households’ debt under microscope. Sabos finansdag, Operaterrassen, Stockholm

  • Stiglitz JE (1997) The role of government in the economies of developing countries. In: Keynote address to the annual world bank conference on development economics. The World Bank, Washington, DC

  • Svensson LE (2014a) The household debt ratio is an unsuitable risk measure–there are much better ones. Eknomistas platform: speeches. https://ekonomistas.se/

  • Svensson LE (2014b) Resilience, debt, and net worth: has resilience increased with higher debt-to-income ratios? Tech. rep., The Institute for International Economic Studies, Stockholm University

  • Taylor JB (1993) Discretion versus policy rule in practice. J Monet Econ 39:195–214

    Google Scholar 

  • Teglio A, Raberto M, Cincotti S (2010) Balance sheet approach to agent-based computational economics: the Eurace project. In: Combining soft computing and statistical methods in data analysis, advances in intelligent and soft computing, vol 77. Springer, Berlin, pp 603–610

  • Teglio A, Raberto M, Cincotti S (2012) The impact of banks’ capital adequacy regulation on the economic system: an agent-based approach. Adv Complex Syst 15(supp02):1250,040

    Article  Google Scholar 

  • Tesfatsion L (2003) Agent-based computational economics: modeling economies as complex adaptive systems. Inf Sci 149(4):262–268

    Article  Google Scholar 

  • Tesfatsion L, Judd K (2006) Agent-based computational economics, handbook of computational economics, vol 2. North Holland

  • Tibaijuka A (2013) Building prosperity: housing and economic development. Routledge, Abingdon

    Book  Google Scholar 

  • Yeh A, Twaddle J, Frith M (2005) Basel II: a new capital framework. Tech. rep., Reserve Bank of New Zealand: Bulletin, vol. 68, no. 3

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrea Teglio.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Figs. 181920 and Tables 45.

Fig. 18
figure 18

Frequency of the Loan-to-Value (LTV) ratio of mortgages granted in the U.S. in 2016. Data on Single-Family Properties from the Enterprise Public Use Database Data Dictionary of the Federal Housing Finance Agency (FHFA). The average mortgage loan is around 80% of the total value of the house. Almost 92% of the LTV are above 60%, 52% are above 80%, while 35% are above 90%

Table 4 Sectorial balance sheet matrix
Fig. 19
figure 19

Impact of different LTV values on housing market indicators. The plots are from additional experiments in which we examine the response of our baseline model (ETA = 0.7 and DSTI = 0.3) with respect to a range of different LTV values. For each LTV = 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, we ran 20 seeds. The duration of each run corresponds to a 30-year simulated period, 7200 iterations. The simulation output confirms that the likelihood of fire sales is reduced for lower LTV values, which is in line with empirical findings (Bubb and Krishnamurthy 2015). Note that the the upper panel represents the distribution of fire sale attempts for each LTV scenario. The boxplots in the lower panel presents the distribution of mortgages per capita for each LTV setting. The diamonds in the figures denote the mean. We have undertaken pair-wise comparisons of cumulative distribution functions of each LTV case, conducting the KolmogorovSmirnov test. The test results suggest statistically significant differences, \(p-value \ll 0.001\), for all pairs other than (0.8, 0.9)

Table 5 Sectorial transaction flow matrix
Fig. 20
figure 20

Sensitivity test to differing population sizes. The results presented in the paper are based on a population of 3000 households. We ran new simulations with double (6000) and half (1500) population sizes, while keeping all other parameters constant (parameters ETA = 0.7 and DSTI = 0.3 are used in all seeds for this sensitivity analysis). For each population we ran simulations with ten different seeds. The plots are distributions from three exemplary variables: GDP, wages, and mortgage per capita. We conducted ANOVA tests to compare outcomes. The boxplots and test results demonstrate that results are statistically stable against differing population sizes. The same pattern was observed in other variables that are not displayed here

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ozel, B., Nathanael, R.C., Raberto, M. et al. Macroeconomic implications of mortgage loan requirements: an agent-based approach. J Econ Interact Coord 14, 7–46 (2019). https://doi.org/10.1007/s11403-019-00238-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11403-019-00238-5

JEL Classification

Navigation