Medium term forecasting models for the housing market: The Stockholm case

1978 ◽  
Vol 40 (1) ◽  
pp. 87-106
Author(s):  
Jan R. Gustafsson ◽  
Björn Hårsman ◽  
Folke Snickars
Keyword(s):  
Housing Market ◽  
Medium Term ◽  
Forecasting Models

MEDIUM TERM FORECASTING MODELS FOR THE HOUSING MARKET: THE STOCKHOLM CASE

2005 ◽  
Vol 40 (1) ◽  
pp. 87-108
Author(s):  
Jan R. Gustafsson ◽  
Björn Hårsman ◽  
Folke Snickars
Keyword(s):  
Housing Market ◽  
Medium Term ◽  
Forecasting Models

Using the GM(1,1) model cluster to forecast global oil consumption

2017 ◽  
Vol 7 (2) ◽  
pp. 286-296 ◽  
Author(s):  
Chaoqing Yuan ◽  
Yuxin Zhu ◽  
Ding Chen ◽  
Sifeng Liu ◽  
Zhigeng Fang
Keyword(s):  
Growth Rate ◽  
Model Selection ◽  
Design Methodology ◽  
Oil Consumption ◽  
Medium Term ◽  
Model Cluster ◽  
Content Type ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Term Prediction

Purpose The purpose of this paper is to compare GM(1,1) model, rolling GM(1,1) model and metabolism GM(1,1) model included in the GM(1,1) model cluster and use these models to forecast global oil consumption. Design/methodology/approach Simulated sequences will be generated randomly, and used to test the models included in the GM(1,1) model cluster; and these grey forecasting models are applied to forecast global oil consumption. Findings Effectiveness of these grey forecasting models is proved by random experiments, which explains the model adaptability. Global oil consumption is predicted, and it shows that global oil consumption will increase at a rather big growth rate in the next years. Originality/value The effectiveness of medium-term prediction of these grey forecasting models is analyzed by random experiments. These models are compared, and some basis for model selection is obtained.

scholarly journals Sentiment-based predictions of housing market turning points with Google trends

2016 ◽  
Vol 9 (1) ◽  
pp. 108-136 ◽  
Author(s):  
Marian Alexander Dietzel
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Turning Points ◽  
House Price ◽  
Data Set ◽  
Content Type ◽  
Forecasting Models ◽  
Search Volume ◽  
Out Of Sample ◽  
Google Search

Purpose – Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts. Design/methodology/approach – Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability. Findings – The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes. Practical implications – The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value – This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

scholarly journals Norway

2020 ◽  
Vol 20 (259) ◽  
Author(s):  
Keyword(s):  
Risk Analysis ◽  
Housing Market ◽  
Financial Sector ◽  
Capital Requirements ◽  
Regulatory Capital ◽  
Policy Changes ◽  
Medium Term ◽  
Assessment Program ◽  
Institutional Frameworks ◽  
Bank Solvency

Much of the work of the Financial Sector Assessment Program (FSAP) was conducted prior to the COVID-19 pandemic, with the missions ending on February 13, 2020. Given the FSAP’s focus on medium-term challenges and vulnerabilities, however, its findings and recommendations for strengthening policy and institutional frameworks remain pertinent. The report was updated to reflect key developments and policy changes since the mission work was completed. It also includes a risk analysis that quantifies the possible impact of the COVID-19 crisis on bank solvency. Since the previous FSAP in 2015, the Norwegian authorities have taken welcome steps to strengthen the financial system. Regulatory capital requirements for banks were raised and actions were taken to bolster the weak capital position of insurers. Alongside other macroprudential measures, temporary borrower-based measures for residential mortgages were introduced, which seem to have had some moderating impact on segments of the housing market. The resolution framework was also strengthened, with the implementation of the Bank Recovery and Resolution Directive (BRRD) and the designation of Finanstilsynet (FSA) as the resolution authority.

Economic growth in Spain is set to slow down

2016 ◽  
Keyword(s):  
Economic Growth ◽  
Interest Rates ◽  
Housing Market ◽  
Public Debt ◽  
Job Creation ◽  
European Countries ◽  
Medium Term ◽  
Content Type ◽  
Sustained Growth ◽  
Low Interest Rates

Significance Despite the absence of an effective government since December, the economy has maintained growth above an annualised 3%, higher than in most other Western European countries. Impacts Sustained growth will enable further job creation and reduce unemployment. Low interest rates and higher employment will support demand, especially in the housing market. Public debt will remain high in the short-to-medium term and the deficit will not be reduced to below 3% until 2018, dragging on growth. There is little macroeconomic space for further stimulus, although both the PP and Citizens have pledged to reduce taxes.

scholarly journals ТHE METHODOLOGY FOR INFLATION’ FORECASTING BY THE BANK OF RUSSIA IN THE MEDIUM TERM

2021 ◽  
pp. 41-52
Author(s):  
Natalya TIKHONYUK ◽  
Elena POMOGALOVA
Keyword(s):  
Comparative Analysis ◽  
Dynamic Method ◽  
Var Model ◽  
Var Models ◽  
Medium Term ◽  
Short Term ◽  
Forecasting Models ◽  
Inflation Forecasting ◽  
Exogenous Factors ◽  
Forecasting Inflation

The paper sets out to examine approaches to the forecasting of inflation by a macro market regulator. Various approaches to short-term inflation forecasting, inflation factors and their main channels of influence used by bank regulators in various countries are studied. The shortcomings of the used models for predicting inflation in the post-pandemic economy have been formulated. A comparative analysis of the use of various models has been conducted and solutions for building forecasting models in the medium term have been proposed. The approach has been tested for regional inflation forecasting; calculations of the indicators using VAR model, SARIMA, and dynamic method have been presented.  It is proposed to use extended combined VAR models supplemented with exogenous factors for medium-term forecasting.

Theoretical Justification of Target Volumes of Housing Commissioning (Rostov Region as the Example)

2018 ◽  
Vol 931 ◽  
pp. 845-850 ◽  
Author(s):  
Alexey A. Aksenov ◽  
Olga Y. Shevchenko ◽  
Elena G. Aksenova
Keyword(s):  
Housing Market ◽  
Planning Horizon ◽  
Market Development ◽  
Medium Term ◽  
Theoretical Justification ◽  
Rostov Region ◽  
Quantitative Estimates ◽  
Macroeconomic Indicators ◽  
Target Volumes ◽  
Regional Housing Market

The paper proves the necessity of determining the target volumes of housing commissioning by the example of the Rostov region under various scenarios of macroeconomic development. The methodology of medium-term forecasting of the development of the regional housing market with a planning horizon of 3 years is used. The task is solved in obtaining quantitative estimates of market development with a change in macroeconomic indicators and planned volumes of housing commissioning.

Medium Term Outcome of LINAC-Based Stereotactic Radiosurgery for Vestibular Schwannomas with Marginal Dose of 12.5 Gy

2012 ◽  
Vol 73 (S 02) ◽  
Author(s):  
J. Ellenbogen ◽  
A. Kinshuck ◽  
M. Jenkinson ◽  
T. Lesser ◽  
D. Husband ◽  
...  
Keyword(s):  
Stereotactic Radiosurgery ◽  
Vestibular Schwannomas ◽  
Medium Term ◽  
Term Outcome
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